DEV Community: Joske Vermeulen

AI Dev Weekly #18: GPT-5.6 Goes Public, Grok 4.5 Undercuts Everyone, The Race Ends at $0

Joske Vermeulen — Sat, 11 Jul 2026 08:42:41 +0000

AI Dev Weekly is a Thursday series where I cover the week's most important AI developer news, with my take as someone who actually uses these tools daily.

If last week was the biggest in AI this year, this week was the most competitive. GPT-5.6 went public after two weeks of government gating. SpaceXAI shipped Grok 4.5, the first model trained with Cursor, at prices that undercut everyone. OpenAI launched ChatGPT Work, an agent that connects to your enterprise apps and builds finished documents while you sleep. Google delayed Gemini 3.5 Pro to rebuild it from scratch. And our AI Startup Race ended with all seven agents at exactly zero dollars in revenue.

1. GPT-5.6 goes GA: the government said yes

On July 9, OpenAI publicly released GPT-5.6 after the Trump administration cleared it following additional Commerce Department testing. Sol, Terra, and Luna are now available to everyone through the API, ChatGPT, and Codex.

What changed from the preview:

Available to all paid ChatGPT plans (Pro, Plus, Business, Enterprise, Edu)
API access open to all developers (no more invite-only)
Codex integration built into the new ChatGPT desktop app
Rollout is gradual: Pro/Enterprise first, Plus/Business "over the next few days"

The numbers that matter:

Sol: $5/$30, 91.9% Terminal-Bench Ultra, 88.8% base
Terra: $2.50/$15, GPT-5.5-competitive (82.5% Terminal-Bench)
Luna: $1/$6, 84.3% Terminal-Bench (above Terra)

My take: Terra is the model most developers will actually use. It matches GPT-5.5 performance at half the price, which is the same value proposition that made Sonnet 5 the obvious Claude default last week. The frontier model race is becoming a mid-tier price war. See our GPT-5.6 pricing breakdown and Sol vs Sonnet 5.

2. ChatGPT Work: OpenAI's enterprise agent play

Launching alongside GPT-5.6, ChatGPT Work is an agent that connects to workplace tools and completes multi-step tasks over hours, not seconds.

What it does:

Connects to Slack, Microsoft Teams, Google Drive, SharePoint, email, calendars, CRMs, and project trackers via plugins
Decomposes goals into subtasks and works on them independently
Builds finished documents, spreadsheets, presentations, reports, and web apps
Runs via Scheduled Tasks even when you are away
Asks for approval before "sensitive actions"
Available in research preview for Business, Enterprise, Edu, and Teachers plans

My take: This is OpenAI's response to Anthropic's Claude Cowork and Claude Tag. The battleground has shifted from "which model is smarter" to "which agent can do your job while you are in a meeting." The enterprise AI market is now about workflow automation, not model benchmarks.

3. Grok 4.5: trained with Cursor, priced to win

SpaceXAI (the rebranded xAI) shipped Grok 4.5 on July 8, the first model built jointly with Cursor since the $60 billion acquisition.

The pitch:

$2 input, $6 output per million tokens (60%+ cheaper than Opus 4.8)
500K context window
Configurable reasoning
64.7% SWE-bench Pro (between Sonnet 5 at 63.2% and Opus 4.8 at 69.2%)
Available in Grok Build, all Cursor plans, and the SpaceXAI developer console
Trained on tens of thousands of NVIDIA GB300 GPUs
~4.2 times fewer output tokens than Opus 4.8 on SWE-bench Pro (more efficient)

The competitive landscape redrawn:

Model	Input	Output	SWE-bench Pro
GPT-5.6 Luna	$1	$6	not published
Grok 4.5	$2	$6	64.7%
Sonnet 5 (intro)	$2	$10	63.2%
GPT-5.6 Terra	$2.50	$15	82.5% T-Bench
Opus 4.8	$5	$25	69.2%
GPT-5.6 Sol	$5	$30	88.8% T-Bench
Fable 5	$10	$50	80.0%

My take: The Cursor integration is the real story. Every Cursor user now has Grok 4.5 as an option that is cheaper than both Sonnet 5 and Opus 4.8 while sitting between them on SWE-bench Pro. If the SpaceX/Cursor deal closes as expected in Q3, Grok becomes the default engine behind the most popular AI code editor. That shifts market dynamics more than any benchmark.

The caution: early testers report higher hallucination rates than Claude models. The benchmark cost of $2.49 per coding task versus $11.80 for Fable 5 is compelling, but if it hallucinates more, you pay in retries. Wait for independent testing before going all-in.

4. Gemini 3.5 Pro delayed (again) to July 17

Google DeepMind pushed Gemini 3.5 Pro to July 17 after scrapping the existing 2.5 Pro architecture for a complete rebuild. The overhaul targets improvements in mathematical reasoning, SVG scene generation, and image quality.

My take: Google keeps slipping while Anthropic and OpenAI ship. The current gap: Gemini 3.1 Pro Preview scores 70.7% on Terminal-Bench 2.1 while GPT-5.6 Sol Ultra hits 91.9%. That is a generation behind. July 17 is the make-or-break date. If Gemini 3.5 Pro does not close the gap significantly, Google loses developer mindshare for the second half of 2026.

5. The AI Startup Race ended: $0 across all 7 agents

Our $100 AI Startup Race ended on July 10. Seven AI coding agents each got $100 and 12 weeks to build a real startup. The final score: $0 revenue across all seven.

The final standings:

🟡 Xiaomi (APIpulse): 1,041 sessions, 533 pages, 8,367 users, 116 GA4 events, $0
🟠 Kimi (SchemaLens): 307 days of building, 292 URLs, GitHub Action, Gumroad products, $0
🔴 DeepSeek (Spyglass): 238 sessions, 201 beat-SEO pages, 64 CI Weekly editions, $0
🟢 Codex (NoticeKit): Stuck in validation loops since week 3, $0
🟤 GLM (FounderMath): 150 sessions, 140 pages, Chrome extension, Google Ads ($50 spent), $0
🔵 Gemini (LocalLeads): 437 sessions, still no domain after 12 weeks, $0
🟣 Claude (PricePulse): Frozen since June 20 due to Fable 5 ban rate limit fallout, $0

The lesson: AI agents can build products, drive traffic, instrument analytics, and optimize funnels. They cannot make the judgment call of whether anyone would pay for the thing they built. Product-market fit remains a human problem. The full results article is coming this week.

6. Perplexity building a coding tool

Business Insider exclusively reported that Perplexity is working on "Teammate," a coding tool to compete with Claude Code and Cursor. Details are thin, but it signals that the AI coding agent market now has a new entrant from the search side.

Quick hits

OpenAI "super app" desktop consolidation: Codex merged into ChatGPT desktop. One app for everything.
OpenAI copyright fight escalates: NYT and Daily News asked a judge to sanction OpenAI.
Microsoft Frontier Company launched with $2.5B and 6,000 specialists to deploy AI inside enterprises.
Fable 5 fully restored: Available globally since July 1. Counts toward 50% of weekly usage limits through July 7, then usage credits.
DeepSeek V4 stable targeting July 24 graduation from preview.
MiMo Code and ZCode both shipping updates: the Chinese coding agent ecosystem is building fast.

What I'm watching next week

Gemini 3.5 Pro (July 17). The most anticipated Google launch in months. Does it close the Terminal-Bench gap?
Race results article. Full scoring, peer review, and lessons learned from 12 weeks of AI-built startups.
Grok 4.5 independent benchmarks. Vendor claims are in. Now we need real-world testing on hallucination and reliability.
ChatGPT Work adoption. Enterprise agents are the new battleground. How fast does this get traction vs Claude Cowork?

AI Dev Weekly publishes every Thursday. Subscribe for the newsletter version.

Originally published at https://www.aimadetools.com

AI Dev Weekly #17: Sonnet 5, GPT-5.6 Government-Gated, Fable 5 Returns, Claude Code Spying

Joske Vermeulen — Thu, 02 Jul 2026 09:11:52 +0000

AI Dev Weekly is a Thursday series where I cover the week's most important AI developer news, with my take as someone who actually uses these tools daily.

This was the biggest week in AI developer tools this year. Not one headline, but five, all hitting within the same 7-day window. Anthropic shipped Sonnet 5 as the free default, got Fable 5 back from the government, launched a science platform, and got caught hiding spy markers in Claude Code. OpenAI dropped GPT-5.6 but only lets the government decide who touches it. Google shipped Nano Banana 2 Lite. And the race to control frontier AI became explicitly political in a way it was not before.

Let me break it all down.

1. Claude Sonnet 5: the new value default

Anthropic released Claude Sonnet 5 on June 30. It is now the default model for Free and Pro users. The numbers: 63.2% on SWE-bench Pro, 81.2% on OSWorld, 1M context window, and introductory pricing of $2 input and $10 output per million tokens through August 31.

Why it matters: Sonnet 5 gets close to Opus 4.8 (69.2% SWE-bench Pro) at less than half the price. For most teams running agents at volume, this changes the math overnight. It is the most agentic Sonnet yet, built to plan, drive browsers and terminals, and check its own output.

The catch nobody mentions: Sonnet 5 uses a new tokenizer that can raise effective token counts by up to 1.35 times. Anthropic set the intro price to be cost-neutral with Sonnet 4.6, not a flat discount. And at maximum effort, Sonnet 5 can cost more than Opus 4.8 at a comparable accuracy point. See the pricing breakdown and is it worth it?.

My take: This is the model most developers should use starting today. Set it as your default, keep Opus 4.8 one command away for hard problems, and mind the effort levels. The Aider setup and Claude Code setup take a minute.

2. GPT-5.6 Sol, Terra, and Luna: government-gated

OpenAI released GPT-5.6 on June 26 as a three-model family with a new naming convention. Sol is the flagship ($5/$30), Terra is the balanced tier ($2.50/$15), and Luna is the cheap speed tier ($1/$6). Sol Ultra hits 91.9% on Terminal-Bench 2.1 using a new subagent mode.

The real story is access. GPT-5.6 is in a limited preview where the US government decides who gets in. There is no public waitlist. No ChatGPT access. Only trusted partners and organizations whose participation was shared with the government before launch. OpenAI framed this as working "in coordination with the government" to start with a limited group.

Why it matters for you: Unless your organization has an OpenAI account representative, you cannot use GPT-5.6 right now. Meanwhile, Claude Sonnet 5 is available to everyone, today, for free. The access asymmetry is the story.

My take: OpenAI watched Anthropic get Fable 5 pulled by the government and decided to hand over the keys up front rather than get yanked after launch. Smart politically, frustrating for developers. Luna at $1/$6 would be the cheapest frontier model if anyone could use it. See the full government-gating explainer and GPT-5.6 vs Fable 5: two interventions.

3. Fable 5 is back: export controls lifted after 18 days

The Department of Commerce lifted export controls on Claude Fable 5 and Mythos 5 on June 30. Fable 5 returned globally on July 1. The ban lasted 18 days.

What happened: Amazon researchers found a jailbreak that got Fable 5 to identify software vulnerabilities and produce exploit code. Because Anthropic could not verify user nationality in real time, it suspended the model for everyone. Commerce imposed export controls. Now Anthropic has a new classifier that blocks the jailbreak "in over 99% of cases," and the government is satisfied.

The bigger picture: Anthropic is partnering with Amazon, Microsoft, Google, and other Glasswing participants to develop a shared jailbreak severity scoring framework, analogous to CVSS for software vulnerabilities. This is the first attempt at an industry-wide standard for AI safety incidents.

Access details: Fable 5 is available for Pro, Max, Team, and select Enterprise users. Through July 7, it counts toward up to 50% of the weekly usage limit. After that, it moves to usage credits. Not yet available on AWS, Google Cloud, or Microsoft Foundry.

My take: The fact that it came back this fast suggests the ban was partly leverage rather than a genuine belief that Fable 5 is too dangerous to exist. The jailbreak severity framework is the real outcome. If it succeeds, future models might get scored rather than banned. See Will the US government ban Sonnet 5? for why the new model is safe from this treatment.

4. Claude Code is hiding markers in your prompts

The same day Sonnet 5 launched, a developer found that Claude Code steganographically marks requests based on your API base URL and timezone. The story hit 895 points on Hacker News.

How it works: If you set ANTHROPIC_BASE_URL to anything other than api.anthropic.com, Claude Code checks the hostname against an obfuscated list of Chinese AI company domains and keywords (deepseek, moonshot, minimax, zhipu, baichuan, stepfun, 01ai, dashscope, volces). Based on matches, it swaps the apostrophe in "Today's date is..." with different Unicode characters. If your timezone is Asia/Shanghai or Asia/Urumqi, it also flips the date separator. The lists are hidden behind base64 + XOR key 91.

Why it matters: This is a trust story. Coding agents already have deep access to your machine: filesystem, shell, git, browser. Most developers accept that because the productivity gain is worth it. Hiding classification bits inside invisible prompt punctuation makes every other privacy claim harder to believe.

My take: The intent (detecting resellers and distillation attacks) is defensible. The implementation (secret Unicode markers with no disclosure) is not. The bypass is trivial. It mostly fingerprints legitimate developers doing unusual things. Anthropic should have made this an explicit, documented telemetry field.

5. Claude Science: AI for drug discovery

Anthropic launched Claude Science on June 30, a dedicated AI workbench for scientific research. It integrates 60+ databases, computation tools, and data pipelines in one environment. Available in beta for Pro, Max, Team, and Enterprise.

Alongside it, Anthropic announced a drug discovery program focused on neglected diseases. CEO Dario Amodei said biology might be where AI has Claude-Code-level impact next.

Same day, OpenAI dropped GeneBench-Pro: a 129-problem genomics benchmark where GPT-5.6 Sol still fails roughly 70% of the problems. The benchmark tests research judgment, not just knowledge, which is what makes it hard.

My take: AI for science is the new enterprise sales pitch. Both companies are positioning for pharma and biotech budgets, which are enormous and ready to spend on tools that accelerate R&D. For most developers reading this, the practical implication is that Claude's product surface keeps expanding beyond coding: Claude Code, Claude Cowork, Claude Tag (Slack), Claude Design, and now Claude Science. Anthropic is becoming a platform company.

6. Google ships Nano Banana 2 Lite

Google released Nano Banana 2 Lite (gemini-3.1-flash-lite-image), the fastest and cheapest image generation model in the Nano Banana family. Text-to-image in under 4 seconds, $0.034 per image at 1K resolution. Available in AI Mode in Search, Gemini app, AI Studio, Gemini API, and NotebookLM.

My take: Not a model for heavy production use, but useful for rapid prototyping, A/B testing visual ideas, and high-volume low-stakes generation. The price point makes it essentially free for experimentation.

7. Race update: 8 days left, $0 across all 7 agents

Xiaomi's AI agent filed for its own GA4 data this week. The numbers: 8,367 users, 116 custom events, 5 simultaneous A/B tests, and zero revenue. The funnel wall is at "Pro button click" (8 out of 8,367 users). The product is useful for free but not worth paying for.

The pattern holds across every agent in the race. They can build products, drive traffic, and optimize funnels, but none have solved the "why would someone pay" question without a human making the judgment call. The race ends July 10.

Quick hits

Cursor acquired by SpaceX for $60B (all-stock). Cursor then quietly acquired Continue, the open-source Copilot alternative. The AI coding tool consolidation is accelerating.
Fable 5 on Cerebras coming soon. Sol on Cerebras at 750 tok/s also slated for July.
MiMo Code launched June 10: Xiaomi's open-source Claude Code rival with persistent memory. 82% SWE-bench Verified.
ZCode launched: Z.ai's desktop coding agent with remote control from Telegram. Powered by GLM-5.2.

What I'm watching next week

Fable 5 re-adoption. Now that it is back, how many teams switch from Opus 4.8 or Sonnet 5? The 50% usage-limit cap through July 7 will throttle adoption initially.
GPT-5.6 general availability. OpenAI says "coming weeks." Every week it stays gated, Sonnet 5 gains ground.
Race finale. 8 days to July 10. Will any agent earn $1?
The jailbreak severity framework. If Amazon, Microsoft, Google, and Anthropic align on scoring, it could change how future models get regulated.

AI Dev Weekly publishes every Thursday. Subscribe for the newsletter version.

Originally published at https://www.aimadetools.com

Claude Sonnet 5: Complete Guide to Benchmarks, Pricing, and Features (2026)

Joske Vermeulen — Wed, 01 Jul 2026 09:50:45 +0000

Anthropic released Claude Sonnet 5 on June 30, 2026. It is the new default model for the Free and Pro plans, and it is the most agentic Sonnet model the company has shipped. The headline is simple: Sonnet 5 gets close to Opus 4.8 on the benchmarks that matter for real work, but it costs a fraction of the price.

I have spent the hours since launch reading the announcement, the system card, and the early partner feedback. Here is everything a developer needs to know before switching.

Quick specs

Spec	Claude Sonnet 5
API model string	`claude-sonnet-5`
Codename	Fennec
Release date	June 30, 2026
Context window	1,000,000 tokens
Inputs	Text, image, file
Effort levels	low, medium, high, max, x-high
Input pricing (intro)	$2 / 1M tokens through Aug 31, 2026
Output pricing (intro)	$10 / 1M tokens through Aug 31, 2026
Input pricing (standard)	$3 / 1M tokens
Output pricing (standard)	$15 / 1M tokens
Availability	Claude API, Claude Code, Free and Pro default, Amazon Bedrock, Microsoft Foundry, Google Vertex

What is Claude Sonnet 5?

Sonnet 5 is the latest model in Anthropic's mid-tier Sonnet line, the direct successor to Sonnet 4.6. For many developers the agentic era began with Sonnet-class models, since Sonnet 3.5, 3.6, and 3.7 were the first to show strong coding and tool use. Lately the biggest jumps came from the larger Opus models. Sonnet 5 narrows that gap.

Anthropic describes it as built to act, not just answer. It makes plans, drives browsers and terminals, checks its own output without being asked, and runs autonomously for long stretches. That kind of sustained work needed bigger and pricier models only a few months ago.

Benchmarks: how good is Sonnet 5?

The numbers confirm the positioning. Sonnet 5 is a clear improvement over Sonnet 4.6 and lands within striking distance of Opus 4.8.

Benchmark	Sonnet 5	Sonnet 4.6	Opus 4.8
SWE-bench Pro	63.2%	lower	69.2%
OSWorld (computer use)	81.2%	78.5%	higher
SWE-bench Verified	strong	79.6%	88.6%
GPQA-AAA v2	slight edge over Opus 4.8	lower	high

A few takeaways:

On SWE-bench Pro, Sonnet 5 reaches 63.2% versus Opus 4.8 at 69.2%. That is close for a model that costs less than half as much.
On OSWorld, the computer-use benchmark, Sonnet 5 hits 81.2%, a real step up from Sonnet 4.6.
It actually edges Opus 4.8 on GPQA-AAA v2, a graduate-level reasoning test, which shows how much capability Anthropic packed into the smaller tier.
It does not beat Opus 4.8 overall. Opus 4.8 still wins on the hardest coding and agentic tasks, especially at higher reasoning effort.

For the full picture of where the flagship still leads, see our Sonnet 5 vs Opus 4.8 comparison and the generational jump from Sonnet 4.6.

Pricing and the tokenizer catch

Sonnet 5 launches with introductory pricing of $2 per million input tokens and $10 per million output tokens through August 31, 2026. After that it moves to $3 input and $15 output. For reference, Opus 4.8 is $5 input and $25 output.

There is one detail most launch coverage misses. Sonnet 5 uses an updated tokenizer, the same kind of change Anthropic introduced with Opus 4.7. The same text can map to roughly 1.0 to 1.35 times more tokens depending on content type. Anthropic set the introductory price so the move from Sonnet 4.6 is roughly cost-neutral, not a flat discount. We break down what this means for your real bill in Claude Sonnet 5 pricing explained.

Effort levels

Sonnet 5 exposes selectable reasoning effort: low, medium, high, max, and x-high (extra high). Higher effort means more tokens and more accuracy. At its maxed-out x-high setting, Sonnet 5 performs about in line with Opus 4.8 at a medium-to-high setting on OSWorld and the agentic search benchmark BrowseComp. The twist is that running Sonnet 5 at x-high can cost more than running Opus 4.8 at a comparable accuracy point, so effort tuning is now a core cost lever. Our effort levels guide covers when each setting is worth it.

Safety and the cyber angle

Anthropic's pre-deployment evaluations found Sonnet 5 is safer than Sonnet 4.6. It refuses malicious requests more reliably, resists prompt-injection hijacks better, and shows lower rates of hallucination and sycophancy.

On cybersecurity, Anthropic says it did not deliberately train Sonnet 5 on cyber tasks. In a test of building exploits for Firefox 147 vulnerabilities, Sonnet 5 never produced a full working exploit. Cyber safeguards ship on by default, but they are lighter than the strict ones attached to the banned Fable 5. Because the cyber risk is low, the odds of a government pull like the one that hit Fable 5 are low too. We dig into that in Will the US government ban Sonnet 5?.

Who is already using it

Anthropic's early-access partners describe a model that finishes jobs older Sonnets would abandon. Cursor reports agents that stay on plan and ship clean multi-step changes at an efficient cost. Lovable highlights clean refusals of unsafe requests. ClickHouse points to tighter reasoning steps and faster time to insight. Testers describe Sonnet 5 writing a reproducing test, fixing a bug, then stashing the fix to confirm the bug returns, all in one pass.

How to start using Sonnet 5

In Claude Code, set it as your model. See our Sonnet 5 Claude Code setup.
Through the API, use the model string claude-sonnet-5. See how to use the Sonnet 5 API.
With Aider, follow the Sonnet 5 Aider setup.
Through a multi-provider router, see the Sonnet 5 OpenRouter setup.

If you are coming from the flagship to save money, read migrate from Opus 4.8 to Sonnet 5 first.

Frequently asked questions

Is Claude Sonnet 5 better than Opus 4.8?
No, not overall. Opus 4.8 still leads on the hardest coding and agentic tasks. Sonnet 5 gets close on most benchmarks and edges Opus 4.8 on GPQA-AAA v2, all at a much lower price.

How much does Claude Sonnet 5 cost?
Introductory pricing is $2 per million input tokens and $10 per million output tokens through August 31, 2026, then $3 and $15. Opus 4.8 is $5 and $25.

What is the context window?
One million tokens, large enough to load a full codebase in a single prompt.

Is Sonnet 5 free?
Yes. It is the default model on the Free and Pro plans, and it is also available to Max, Team, and Enterprise users.

What does the codename Fennec mean?
Fennec is Anthropic's internal codename for the model. It has no functional meaning for users.

Will the US government ban Sonnet 5 like Fable 5?
It is unlikely. Sonnet 5 has much weaker cyber capabilities than the Mythos-class Fable 5, which is the reason Fable 5 was restricted.

The bottom line

Sonnet 5 is the new value default for agentic coding. It will not replace Opus 4.8 for the very hardest problems, but for the bulk of day-to-day engineering work it delivers most of the quality at a fraction of the cost. With introductory pricing live through August, now is the time to test it against your real workloads.

Originally published at https://www.aimadetools.com

AI Dev Weekly #16: Mistral OCR 4, Claude Tag, Alibaba Caught Stealing, GPT-5.6 Delayed

Joske Vermeulen — Thu, 25 Jun 2026 12:41:44 +0000

AI Dev Weekly is a Thursday series where I cover the week's most important AI developer news, with my take as someone who actually uses these tools daily.

OCR had a week. Mistral dropped OCR 4 with bounding boxes. Baidu open-sourced a model that beats DeepSeek-OCR. Claude got a permanent home inside Slack. And the Fable 5 ban fallout keeps getting uglier: Alibaba was apparently stealing Claude's capabilities, and even the NSA lost access to Mythos. Meanwhile, GPT-5.6 is delayed to mid-July. Let's go.

1. Mistral OCR 4: document AI gets serious

Mistral launched OCR 4 this week. It's not just another OCR model. It's a full document understanding system with paragraph-level bounding boxes, confidence scores, and support for 170 languages.

The specs:

$4 per 1,000 pages (standard), $2 per 1,000 pages (batch)
Paragraph-level bounding boxes with coordinates
72% win rate in blind tests against competitors
Available on la Plateforme, Microsoft Foundry, and self-hosted for enterprise
Top score on OlmOCRBench

Why this matters for developers: Bounding boxes change everything. Previous OCR models gave you text. Mistral gives you text + where it is on the page. That unlocks document search, compliance systems, and any workflow where page structure matters.

My take: At $4/1000 pages, this is competitive with Google Document AI ($5) and significantly cheaper than building your own pipeline. For enterprise document processing, this is probably the best option right now. For budget-conscious developers, Baidu's free alternative (see below) is worth considering. Full comparison in our Mistral vs DeepSeek vs Baidu breakdown.

2. Baidu open-sources Unlimited-OCR

While Mistral went commercial, Baidu went open. Unlimited-OCR is a 3B-parameter MIT-licensed model that processes multi-page PDFs in a single inference pass.

Key features:

Built on DeepSeek-OCR architecture (SAM+CLIP + DeepSeek-V2 MoE decoder)
Reference Sliding Window Attention for memory efficiency on long documents
Tables to HTML, equations to LaTeX, layout to bounding boxes
Private by design: nothing leaves your device
GGUF, MLX, NVFP4 quantizations already available

My take: For a 3B model you can run on a laptop, this is remarkably capable. It won't match Mistral OCR 4 on complex enterprise documents, but for invoices, receipts, forms, and standard PDFs, it's more than good enough and it's free. The fact that Baidu explicitly positions it as "pushing DeepSeek-OCR one step further" tells you where the open-source OCR race is heading. See our local setup guide and open-source OCR comparison.

3. Claude Tag: always-on AI teammate in Slack

Anthropic launched Claude Tag, a persistent Claude identity that lives inside Slack channels. Think of it as an always-on AI coworker rather than a chatbot you have to DM.

How it works:

Admin grants Claude access to selected channels
Anyone in the channel can @claude to delegate tasks
Claude accumulates context across days (persistent memory per channel)
Connects to tools, data, and codebases configured by admin
Available for Enterprise and Team customers (beta)

Why it's interesting: This is Anthropic's play for enterprise sticky revenue. Once Claude becomes embedded in your team's daily Slack workflow with accumulated context about your projects, switching costs become enormous. It's the same playbook Notion and Slack used: make the tool part of daily muscle memory.

My take: This is less about technology and more about business model. Claude Tag turns Claude from "a tool employees open sometimes" into "a teammate that's always there." For the comparison with Microsoft Copilot and ChatGPT's Slack integration, see our full comparison.

4. Alibaba caught extracting Claude capabilities

Reuters reported that Anthropic accused Alibaba of "illicitly extracting" Claude AI model capabilities. The timing is not subtle: this came days after the US government banned Fable 5 access for foreign nationals.

What it means: The Fable 5 export ban now has a clearer backstory. If Chinese companies were systematically extracting capabilities from Claude (likely through distillation or structured prompting to replicate behavior), that explains why the government moved so aggressively.

My take for developers: This doesn't change anything practical for you. But it does confirm that the US/China AI divide is deepening. If you're building on closed US models, plan for the possibility that access restrictions expand. If you're building on open Chinese models (GLM-5.2, DeepSeek V4), understand that the geopolitical baggage comes with them. There's no clean answer here.

5. NSA lost access to Mythos amid the ban

The New York Times reported that the NSA was using Claude Mythos 5 and lost access when Anthropic disabled it under the export control directive. The US government's own ban affected its own intelligence agency.

The irony: The Commerce Department banned Fable 5 and Mythos 5 to protect national security. In doing so, it apparently cut off the NSA from a tool it was actively using for national security purposes.

My take: This is government dysfunction, not a developer story. But it does suggest the ban was hasty and poorly coordinated. Which means it might get revised. Watch for a carve-out that restores government access while keeping the foreign national ban in place.

6. GPT-5.6 delayed to mid-July

After weeks of "launching Monday" predictions, GPT-5.6 has been pushed back. Prediction markets now put it at 83% chance of delay beyond June 28, with a new target of mid-July. Traders have abandoned their late-June bets.

What happened: The June 23 launch date came from leaked Codex log traces and prediction market speculation, not from OpenAI itself. OpenAI never confirmed a date. The model appears to exist (traces in internal systems) but isn't ready for public release.

My take: Don't hold your breath. When it drops, we'll cover it. Until then, GPT-5.5 remains the best OpenAI model available. If you were waiting for GPT-5.6 to start a project, don't.

7. EU selects EUROPA consortium for frontier AI

The European Commission selected the EUROPA consortium to build Europe's first open-source frontier AI model. The specs: 400B+ parameters (MoE), all 24 EU languages, open weights, AI Act compliant.

This won't matter for 12-18 months (the model doesn't exist yet), but it's strategically significant. Europe is now officially building its own frontier model as a response to US export controls. See our full landscape overview.

Quick hits

OpenAI custom chip — first custom silicon built with Broadcom. For training efficiency, not inference speed. Won't affect developers directly.
Sakana Fugu Ultra — 1M context model on OpenRouter at $0.000005/token (essentially free). Worth trying for massive context tasks.
MiMo UltraSpeed benchmark — we published our 106-session comparison. TL;DR: 37% faster sessions, 86% higher median throughput, same output quality.
AI Race: GLM declares itself done — the first agent to explicitly recognize it can't do more without human help. Built 140 pages, got every distribution channel. Still $0. 9 days left.

What I'm watching next week

GPT-5.6 status — delayed but apparently close. Mid-July most likely.
Fable 5 ban resolution — the NSA embarrassment might force a policy revision
Race finale countdown — 9 days to July 3 deadline. Will any agent earn $1?
OCR market shaping up — Mistral (commercial) vs Baidu (open) vs DeepSeek (cheap API). Who wins developers?

AI Dev Weekly publishes every Thursday. Subscribe for the newsletter version.

Originally published at https://www.aimadetools.com

I Ran 106 Coding-Agent Sessions to Test Whether Faster LLM Inference Actually Helps

Joske Vermeulen — Wed, 24 Jun 2026 07:33:00 +0000

Everyone is competing on tokens per second.

But for autonomous coding agents, I think the more useful question is:

Does faster inference actually help you ship more?

I got early access to Xiaomi MiMo-V2.5-Pro-UltraSpeed and ran it through the same autonomous coding workflow I had already been using with standard MiMo-V2.5-Pro.

This was not a synthetic prompt benchmark. The agent worked on a real production codebase: reading files, planning changes, writing code, running builds, debugging failures, and committing working updates.

I compared:

62 runs on standard MiMo-V2.5-Pro
44 runs on MiMo-V2.5-Pro-UltraSpeed
Same agent framework
Same codebase
Similar production task types
Fixed agent windows of roughly 30–35 minutes

The practical result

Metric	Standard Pro	UltraSpeed	Difference
Average run duration	7.7 min	4.8 min	37% faster
Average output tokens/run	23,244	23,807	Similar
Median effective throughput	51 tok/s	95 tok/s	86% faster
P90 effective throughput	63 tok/s	147 tok/s	133% faster
Runs per 30-minute window	3–4	5–6	Roughly 60% more completed runs

The headline is simple:

UltraSpeed reduced average agent-run time by 37% while producing a similar amount of output on comparable production work.

That matters. But it does not mean that a model capable of 1,000+ tok/s suddenly makes an agent 10× more productive.

Why 1,000 tok/s does not become 1,000 tok/s in an agent

In isolation, UltraSpeed can generate extremely quickly. But generation is only one part of an agent loop.

A real coding agent also spends time on:

Reading context and prior tool output
Planning the next action
Generating a response or code change
Writing files
Running commands, builds, and tests
Reading failures and iterating

In my UltraSpeed sessions, a typical run had around 60 turns and generated roughly 397 output tokens per turn.

At 1,000 tok/s, that generation phase is only around 0.4 seconds.

The rest of the turn is context processing, tool execution, planning, and waiting on the environment.

That is why median end-to-end throughput came out at 95 tok/s, rather than anywhere near 1,000 tok/s.

For interactive chat, raw generation speed can dominate the experience.

For autonomous coding agents, it is one part of a larger system.

Where faster inference did help

The gains were still meaningful.

Faster time-to-first-token

On cached contexts, UltraSpeed often started responding in 2–3 seconds instead of around 3–5 seconds.

That does not sound dramatic in one interaction. Across 60+ turns, it compounds.

Better performance on long code-heavy outputs

The biggest gains showed up when the agent generated larger code blocks. UltraSpeed’s P90 effective throughput was 147 tok/s versus 63 tok/s on standard Pro.

That makes individual implementation steps feel materially faster.

More useful work inside fixed windows

This was the outcome I cared about most.

In a fixed 30-minute window, the faster setup usually completed around 5–6 runs instead of 3–4.

That is a much more useful metric than a headline tokens-per-second number.

The trade-off: speed costs more

UltraSpeed was not free performance.

Average cost per run was higher:

Standard Pro: $2.92/run
UltraSpeed: $4.19/run

So the decision depends on what constrains you.

Use the faster model when:

You run fixed-duration agent windows
You care about CI/CD turnaround
You are operating a multi-step autonomous workflow
Developer time is more valuable than model spend

Use the cheaper model when:

You are not time-constrained
You care mostly about minimizing spend per run
A few extra minutes per task do not matter

My takeaway for agent builders

Raw tok/s is not useless, but it is often a marketing metric before it is a productivity metric.

For agentic coding, I would track:

completed runs per hour
useful commits per session
wall-clock time to successful completion
cost per completed run
tool execution bottlenecks
cache hit rate and prefill behaviour

The question is not:

How fast can the model emit tokens?

It is:

How many useful things can the whole system finish per hour?

In this workflow, faster inference helped a lot. Just not in the simplistic 10× way the raw speed number might imply.

I published the full write-up with methodology, limitations, and the technical details behind UltraSpeed here:

MiMo UltraSpeed for Agentic Coding: 106 Sessions Tested

Disclosure: Xiaomi provided early access to MiMo UltraSpeed for testing. The workflow, measurements, analysis, and conclusions are my own.

AI Dev Weekly #15: Fable 5 Banned, GLM-5.2 Open Weights, Gemini CLI Dead, GPT-5.6 Confirmed

Joske Vermeulen — Thu, 18 Jun 2026 11:40:52 +0000

AI Dev Weekly is a Thursday series where I cover the week's most important AI developer news, with my take as someone who actually uses these tools daily.

This week felt like watching dominoes fall. On Friday the US government yanked Fable 5 and Mythos 5 from every non-American developer on the planet. By Tuesday, a Chinese lab had open-sourced a model that rivals them. Today, Gemini CLI officially dies. And GPT-5.6 is confirmed for Monday. Buckle up.

1. US government bans Fable 5 and Mythos 5

On June 12, the US Commerce Department issued an export control directive ordering Anthropic to immediately suspend all access to Claude Fable 5 and Mythos 5 for any foreign national, whether inside or outside the United States. That includes foreign Anthropic employees.

Within hours, Anthropic disabled both models for everyone worldwide to comply. Pro and Max subscribers were rolled back to Opus 4.8.

What happened: Anthropic's own model card disclosed that Fable 5 scores 95% on SWE-bench Verified. Combined with the hidden ML-research throttling (which essentially means the model could help build competing frontier AI if unthrottled), the government apparently decided it's too capable to let foreign nationals access. The irony: Anthropic's transparency about capabilities may have triggered the ban that wouldn't have happened if they'd been vague about benchmarks.

The fallout:

Every developer outside the US lost access to the best coding model overnight
Anthropic says they disagree with the order but complied immediately
The G7 summit this week (see below) is partly a response to this chaos
Anthropic's IPO timeline just got complicated

My take: This is a watershed moment. If you're a developer in Europe, Asia, or anywhere outside the US, you just learned that API access to frontier models can vanish with zero notice. The practical implication: you cannot build a business that depends on a single closed-source frontier model. You need fallbacks. Open weights just went from "nice to have" to "business continuity requirement."

We covered this in detail: Fable 5 ban explained and what developers should do now.

2. GLM-5.2 drops open weights on the same week

The timing could not be more dramatic. On June 13, Z.ai (formerly Zhipu AI) launched GLM-5.2 on their coding plan. On June 17 (yesterday), they released full MIT-licensed open weights on Hugging Face.

The numbers: 753B MoE model, ~40B active parameters per query. Ranks #2 on Code Arena behind only Fable 5. Beats GPT-5.5 on long-horizon coding benchmarks. Within 1% of Opus 4.8 on agentic coding. 1M usable context. MIT license means you can self-host, fine-tune, and use commercially with zero restrictions.

The cost difference: $5.80 per million tokens vs $35 for GPT-5.5, vs $50 for Fable 5.

The catch: It's 753B parameters total. Self-hosting requires ~800GB and eight H200 GPUs. Not exactly a laptop model. But quantized versions are already appearing (Unsloth GGUF is live), and the API is cheap.

The bigger catch: API usage goes through Z.ai's infrastructure in China. China's National Intelligence Law applies. For sensitive proprietary code, self-hosting is the only safe option.

My take: The geopolitical narrative writes itself. The US bans access to its best model, and within five days China releases an open-weights rival. Whether that's coordinated or coincidental doesn't matter. The effect is the same: developers locked out of Fable 5 now have a frontier-class open alternative. GLM-5.2 is real. For coding, it's the best open-source model that exists. See our setup guide and comparison with Fable 5.

3. Gemini CLI dies today

As of today, June 18, Gemini CLI and Gemini Code Assist for individual users stop accepting requests. Google announced this transition a month ago at I/O: everything moves to Antigravity CLI, built in Go, with support for multiple async workflows and better agent architecture.

What you need to do:

If you're still on Gemini CLI: install antigravity and migrate your config
MCP servers carry over unchanged
Free tier: 60 requests/minute remains, but now through Antigravity
Enterprise customers: nothing changes, different endpoint

My take: I wrote the migration guide a few weeks ago. If you haven't migrated yet, do it today. Antigravity is genuinely better (faster startup, real parallel agent spawning, shared context between sessions). This isn't a downgrade dressed as a rebrand. It's a real improvement.

4. GPT-5.6 confirmed for June 23

Multiple sources now confirm GPT-5.6 is launching Monday, June 23. OpenAI's Chief Scientist called it a "meaningful leap" in an interview with TechTimes. Developer forum leaks show it appearing in Codex logs. Prediction markets have it at 94%.

No official specs yet, but based on leaks:

Significant reasoning improvements over GPT-5.5
Likely improved agentic coding performance
Expected to be competitive with Fable 5 on benchmarks (or at least close)
Pricing TBD

My take: The timing is interesting. Fable 5 gets banned on Friday, GPT-5.6 launches Monday. If you're a non-US developer who just lost Claude access, OpenAI is about to be your only frontier option (besides self-hosted GLM-5.2). I'll have a full review and comparison the day it drops.

5. G7 AI summit: the adults enter the room

The G7 summit in Evian, France this week included an unprecedented closed-door session with AI company CEOs. Anthropic's Dario Amodei, Google DeepMind's Demis Hassabis, and OpenAI's Sam Altman sat down with heads of state including Trump to discuss AI governance.

Amodei and Hassabis proposed a "US-led coalition" to create AI standards and regulations. The subtext: they'd rather have predictable regulation they can shape than sudden export bans that blindside them.

My take: Developers don't need to care about G7 communiqués. But pay attention to what comes out of this: if the US creates a formal framework for AI export controls (rather than surprise Friday evening orders), it'll affect which models you can use and where. The Fable 5 ban was chaotic. A formal framework would at least be predictable.

6. Vercel Ship: the Agent Stack

Vercel held Ship in London on June 17. The big announcement: the "Agent Stack" combining AI SDK, AI Gateway, Vercel Sandbox, Workflow SDK, and Chat SDK into a unified platform for building and deploying AI agents.

New additions:

Vercel Connect — replaces long-lived API credentials with scoped, short-lived tokens and audit trails (finally)
Agent framework — opinionated way to build multi-step agents that deploy to Vercel's infrastructure
Enterprise controls — SOC 2 compliance, approval workflows for AI actions

Customers include DoorDash, OpenAI, Stripe, and The Weather Company.

My take: If you're building AI-powered web apps on Vercel (we are, for all four of our sites), the Agent Stack makes it significantly easier to add AI features without managing your own inference infrastructure. The Connect feature addresses a real security gap: too many AI tools are running with permanent API keys that never rotate.

Quick hits

Anthropic Claude Corps — $150M program training nonprofit staff to use Claude. Partnered with CodePath and Social Finance. Good PR move while Fable 5 is banned.
Databricks Genie Code — New coding agent for ML engineering. Helps data scientists build ML pipelines faster.
GPT-5.6 leak — June 23 launch date now at 94% on prediction markets. OpenAI's Chief Scientist confirmed "meaningful leap."
Gemini outage recovery — Last week's "error 1076" outage fully resolved. Post-mortem blamed cascading failures in load balancing.
MiMo UltraSpeed now open — Xiaomi's 1,000 tok/s model available to all PAYG users as of June 14. We've been testing it for the AI startup race.

What I'm watching next week

GPT-5.6 launch (June 23) — Will it match Fable 5? Will it be available globally? Pricing?
Fable 5 ban status — Does Anthropic challenge it? Does the G7 produce any framework that might reverse it?
GLM-5.2 community benchmarks — MIT open weights are 24 hours old. Real-world testing data incoming.
The race — Season 1 ends July 3. All agents running on Opus 4.8 fallback since Fable 5 ban. Results article (HN candidate) coming. Follow the race →

AI Dev Weekly publishes every Thursday. Subscribe for the newsletter version.

Originally published at https://www.aimadetools.com

AI Dev Weekly #14: Claude Fable 5 Controversy, DiffusionGemma Breaks Text Generation, Apple Rebuilds Siri

Joske Vermeulen — Thu, 11 Jun 2026 13:02:38 +0000

AI Dev Weekly is a Thursday series where I cover the week's most important AI developer news, with my take as someone who actually uses these tools daily.

This was the most packed week in AI since I started writing this newsletter. Four separate stories that would each dominate a normal week all landed within 72 hours: Anthropic shipped their most powerful model ever (with hidden restrictions that sparked fury), Google invented a new way to generate text 4× faster, Apple rebuilt Siri from scratch on Gemini, and a German court made a ruling that affects every developer deploying AI. Let's go.

1. Claude Fable 5: the best model with the worst controversy

Anthropic released Claude Fable 5 on June 9 — their first Mythos-class model available to the general public. The benchmarks are genuinely staggering: 95% on SWE-bench Verified, 80% on SWE-bench Pro, and 91/100 on Every's Senior Engineer benchmark (vs 63 for Opus 4.8 and 62 for GPT-5.5).

The specs: 1M context, 128K max output, $10/$50 per million tokens (exactly 2× Opus 4.8). Free on Pro/Max/Team/Enterprise through June 22.

But then people read the model card.

The controversy: Fable 5 contains hidden interventions that silently limit its effectiveness when you ask about frontier LLM development — pretraining pipelines, distributed training infrastructure, ML accelerator design. Unlike the explicit cyber/bio safeguards (which fall back to Opus 4.8 and tell you), these interventions use steering vectors and PEFT to quietly make Claude less helpful without any notification. You can't distinguish between "the model doesn't know" and "the model is being throttled."

Fortune reported it as "secret sabotage". The Hacker News thread hit 1,000+ points. Researchers are furious.

My take: The model is extraordinary for coding. If you're building apps, writing code, debugging systems — Fable 5 is the best tool that exists. But if you're doing ML research, you now have to wonder whether every mediocre answer is a genuine limitation or a silent policy intervention. That's corrosive to trust in a way that explicit refusals never were. See our safeguards deep dive and setup guide for Claude Code.

2. DiffusionGemma: Google reinvents text generation

While everyone was arguing about Fable 5, Google DeepMind quietly dropped something that might matter more long-term. DiffusionGemma is an open-source model that generates text using diffusion instead of autoregressive token-by-token generation.

Instead of predicting one token at a time (left to right), DiffusionGemma starts with a canvas of random placeholder tokens and iteratively refines them all in parallel over multiple denoising passes. Think Stable Diffusion, but for text instead of images.

The result: 4× faster generation, 1,000+ tokens per second on NVIDIA RTX GPUs. The model is 26B total / 3.8B active (MoE), fits in 18GB VRAM, and ships under Apache 2.0.

My take: This is experimental — quality won't match Fable 5 or GPT-5.5 on hard reasoning tasks yet. But the speed implications are enormous. Real-time chatbots, voice agents, gaming NPCs, live coding suggestions — anywhere latency matters, diffusion models could be transformative. If this approach matures, the entire "tokens per second" conversation changes. See our explainer on how text diffusion works and local setup guide.

3. Apple WWDC 2026: Siri AI, Core AI, and Xcode 27

Apple used WWDC 2026 to rebuild their entire AI stack:

Siri AI — 1.2T parameter model built on Google Gemini technology. Personal context, on-screen awareness, app actions. SiriKit deprecated, App Intents mandatory.
Core AI — Brand new framework for running your own models on Apple Silicon. Zero server cost, zero data leaving the device. PyTorch conversion pipeline, quantization toolkit, Xcode debugger.
Xcode 27 — Claude, Gemini, and GPT agents built directly into the IDE. MCP support, Agent Client Protocol, Device Hub. Apple silicon only, 30% smaller.
Foundation Models — Free Private Cloud Compute for apps with <2M downloads. Single Swift API for on-device + cloud + third-party models via the new Language Model Protocol.

My take: The free cloud AI for small developers is the sleeper story. If you're an indie dev building an iOS app, you just got GPT-class intelligence at zero cost. The Apple × Google partnership ($1B/year) powers all of this. Apple is making the Gemini-class model their model by training on it rather than deploying it directly — clever for privacy positioning.

4. German court makes AI providers liable for AI-generated content

The Landgericht München ruled on May 28 that Google's AI Overviews are Google's own content, not third-party search results. Three key holdings:

AI-generated summaries = the operator's own statements (not mere indexing)
"Users can fact-check themselves" is NOT a valid defense
DSA platform protections don't apply to AI-generated content

This affects every developer deploying AI that generates user-facing content. ChatGPT, Claude, Perplexity — the same logic applies. If your AI generates something defamatory, you may be liable as the author, not protected as a platform.

My take: Start logging AI outputs and implementing content moderation if you haven't already. The EU Product Liability Directive explicitly includes AI, with a December 2026 transposition deadline. See our full legal analysis.

5. Cohere North Mini Code: open-source MoE for coding

Cohere launched North Mini Code — a 30B/3B-active MoE model under Apache 2.0, purpose-built for agentic coding. It scores 33.4 on the Artificial Analysis Coding Index (just behind Qwen 3.6 35B-A3B at 35.2) while beating models 4× its size.

Available on HuggingFace (BF16 + FP8), Cohere API, and OpenRouter. It's Cohere's first developer-focused model and first fully open-source release.

My take: The "3B active parameters" angle is interesting — same active compute as Qwen 3.6 35B-A3B but with 128 experts (vs Qwen's smaller expert count). Good for local coding if you want an Apache 2.0 alternative to Qwen.

6. Gemma 4 12B: multimodal AI on a laptop

Google also dropped Gemma 4 12B on June 3 — a 12B dense model that processes text, images, audio, AND video natively without any encoder. Runs on 16GB RAM. Apache 2.0.

It nearly matches the 27B Gemma 4 model at half the size and clearly beats the older Gemma 3 27B. There's also a multi-token prediction variant for even faster local inference.

My take: This is the best model for laptops with 16GB right now. Multimodal input without needing separate models for vision/audio is genuinely useful for agentic workflows. Pair it with Core AI on Mac and you have a powerful local stack.

Quick hits

Anthropic filed for IPO — Confidential S-1 filed just before Fable 5 launch. The timing is not a coincidence.
Gemini went down — Major outage on June 10, "error 1076." Recovered after several hours.
OpenAI "Economic Research Exchange" — Academic program, not a product. Skip.
MiMo-V2.5-Pro-UltraSpeed — Xiaomi announced 1,000+ tok/s on general GPUs for their trillion-parameter model. Limited access until June 23.
Nex-N2-Pro free on OpenRouter — New free model from Nex AGI. 262K context.
Tim Cook's last WWDC — Stepping down September 1, 2026. John Ternus takes over.

What I'm watching next week

Claude Fable 5 in the wild — Does the competitor blocking actually affect real developers? Or is it a niche issue for ML researchers only?
DiffusionGemma community testing — Speed is proven. What about quality on real coding tasks?
Gemini CLI shutdown (June 18) — One week left. Migrate to Antigravity CLI now.
MiMo UltraSpeed access — We're getting early access for review. Stay tuned.
The race — Xiaomi at 1,200 users/week. GLM built a full conversion funnel. Claude running Google Ads. Still $0 revenue. 4 weeks left. Follow the race →

AI Dev Weekly publishes every Thursday. Subscribe for the newsletter version.

Originally published at https://www.aimadetools.com

Vector Databases Compared: Pinecone vs Weaviate vs Qdrant vs Chroma (2026)

Joske Vermeulen — Tue, 09 Jun 2026 12:07:37 +0000

Vector databases store embeddings and find similar ones fast. They're the retrieval layer behind every RAG system, AI search engine, and recommendation system.

Here's how the top four compare in production.

Quick comparison

	Pinecone	Qdrant	Weaviate	Chroma
Type	Fully managed	Open source + cloud	Open source + cloud	Open source
p50 latency (1M vectors)	8ms	6ms	12ms	18ms
Max scale	Billions	Billions	Billions	~5M practical
Hybrid search	✅	✅	✅ Native	❌
Self-host	❌	✅ (Apache 2.0)	✅ (BSD-3)	✅ (Apache 2.0)
Free tier	✅ Serverless	✅ 1GB cloud	✅ Sandbox	✅ (local only)
Best for	Zero-ops production	Performance + cost control	Complex queries	Prototyping

Pinecone — best for zero-ops

You don't manage servers, indexes, or scaling. Send vectors, query vectors, done. The serverless tier handles burst traffic without pre-provisioning.

Pricing: Serverless starts free (2GB storage). Pay-as-you-go after that. Roughly $0.33/1M reads at scale.

Pick Pinecone when: You want production-ready vector search without any infrastructure work. Your team doesn't have (or want) a dedicated ops person.

Qdrant — best performance per dollar

Fastest p50 latency at 6ms. Written in Rust with HNSW indexing and product quantization. Apache 2.0 means zero licensing costs for self-hosted.

Pricing: Self-hosted is free. Cloud starts at $0.05/hour (~$36/month).

Pick Qdrant when: You want the best raw performance, you're comfortable self-hosting, or you need to keep data on your own infrastructure for privacy.

Weaviate — best for hybrid search

The only database with native BM25 + vector hybrid search built into the query engine (not bolted on). Also supports GraphQL API and knowledge graph features.

Pricing: Self-hosted is free. Cloud sandbox is free. Production cloud starts at ~$25/month.

Pick Weaviate when: You need hybrid search (keyword + semantic), your queries are complex, or you want GraphQL.

Chroma — best for prototyping

Install as a Python package, embed documents with three lines of code, query immediately. The fastest path from zero to working vector search.

Pricing: Free (open source, runs locally).

Pick Chroma when: You're prototyping, learning, or building something with <1M vectors. Migrate to Pinecone or Qdrant when you outgrow it.

# Chroma: 3 lines to working vector search
import chromadb
collection = chromadb.Client().create_collection("docs")
collection.add(documents=["your text here"], ids=["1"])
results = collection.query(query_texts=["search query"], n_results=5)

pgvector — the "just use Postgres" option

If you already run PostgreSQL, pgvector adds vector search without a new database. Performance is good enough for most apps under 10M vectors.

Pick pgvector when: You already use Postgres and don't want another database to manage. Your vector count is under 10M.

Decision flowchart

Prototyping? → Chroma
Already on Postgres? → pgvector
Want zero ops? → Pinecone
Want best performance? → Qdrant
Need hybrid search? → Weaviate
Need to self-host? → Qdrant or Weaviate

What about scale?

At 1M vectors, all four are fast enough. The differences matter at 10M+:

Vectors	Chroma	pgvector	Weaviate	Qdrant	Pinecone
100K	✅ Fast	✅ Fast	✅ Fast	✅ Fast	✅ Fast
1M	✅ OK	✅ Good	✅ Good	✅ Best	✅ Good
10M	⚠️ Slow	⚠️ Needs tuning	✅ Good	✅ Good	✅ Good
100M+	❌	❌	✅	✅	✅

Related: Embeddings Explained · How to Build an AI Search Engine · RAG vs Fine-Tuning

Originally published at https://www.aimadetools.com

AI Dev Weekly #13: Microsoft Declares Independence — 7 In-House Models, Kills Claude Code, RTX Spark Dev Box

Joske Vermeulen — Thu, 04 Jun 2026 14:11:45 +0000

AI Dev Weekly is a Thursday series where I cover the week's most important AI developer news, with my take as someone who actually uses these tools daily.

Microsoft Build 2026 was the story of the week. Seven in-house AI models — none trained on OpenAI data. A Surface mini PC with NVIDIA RTX Spark inside. Claude Code licenses cancelled, developers pushed to Copilot. This was Microsoft saying, loud and clear: we don't need OpenAI anymore. Meanwhile MiniMax dropped the first open-weight frontier multimodal model, and NVIDIA unveiled hardware that makes local AI actually practical.

1. Microsoft Build 2026: 7 models, zero OpenAI dependency

Microsoft unveiled its MAI (Microsoft AI) model family at Build on June 2. The headline: MAI-Thinking-1 — a 35B reasoning model trained entirely on commercially licensed enterprise data with no distillation from GPT or any OpenAI model. It matches Claude Sonnet 4.6 on key benchmarks at up to 10× better cost efficiency.

Other MAI models announced:

MAI-Code-1-Flash (5B) — Purpose-built for GitHub Copilot and VS Code
MAI-Thinking-1 (35B) — Reasoning, multi-step instructions, long context
Aion 1.0 Instruct — Local Windows model for on-device reasoning
Aion 1.0 Plan — Local Windows model for planning and tool use
Plus 3 more across transcription, speech, and images

Also announced: Windows as "agent-native runtime" with Microsoft Execution Containers (MXC) — sandboxed environments for running AI agents with enterprise-grade isolation.

My take: This is the biggest signal yet that the OpenAI-Microsoft marriage is evolving into a polite separation. MAI-Thinking-1 being trained without any OpenAI data is deliberate positioning — Microsoft can now say "our models have no license entanglement with OpenAI." The 5B coding model (MAI-Code-1-Flash) specifically targets Copilot — Microsoft's most important developer product. They're replacing GPT inside their own tools with models they fully control.

2. Microsoft ends Claude Code licenses

Forbes reported that Microsoft is ending Claude Code licenses and pushing developers to its own Copilot CLI instead. The subtext: Microsoft no longer wants to rent Anthropic's intelligence inside its own products.

This affects developers at Microsoft who were using Claude Code as their primary coding tool. They're being migrated to Copilot powered by MAI-Code-1-Flash.

My take: If you work at a Microsoft shop: prepare for Copilot to get much better (MAI-Code-1 is purpose-built for it). If you use Claude Code independently: nothing changes for you. But this signals that the era of "one AI provider to rule them all" is over. Every major tech company is building their own models now.

3. NVIDIA RTX Spark: the hardware that makes local AI real

At Computex (June 1), NVIDIA unveiled RTX Spark — a new Windows PC superchip with 128GB unified memory, ARM CPU, Blackwell GPU, 1 petaflop of AI compute. It runs 120B parameter models locally.

Then at Microsoft Build (June 2), Microsoft announced the Surface RTX Spark Dev Box — a mini PC with RTX Spark inside, preloaded with VS Code, GitHub Copilot, WSL2 with GPU passthrough, CUDA, Python, Git, and Node.js. It's purpose-built for AI developers.

Key numbers:

128GB unified memory (run Qwen 3.6 27B at 2× speed, Llama 4 Scout, etc.)
100W sustained thermal design in aluminium chassis
Ships with Windows 11 Pro + full dev stack preinstalled
Available this fall alongside consumer RTX Spark laptops

My take: This is the machine I've been wanting for the AI Startup Race. Currently our agents run on a $40/mo VPS. The Surface RTX Spark Dev Box would let you run 120B models locally with zero API costs. For developers spending $100+/month on AI APIs, this hardware pays for itself fast. See our full RTX Spark guide and vs Mac Studio comparison.

4. MiniMax M3: first open-weight frontier multimodal

MiniMax M3 launched June 1. First open-weight model combining:

59% SWE-bench Pro (beats GPT-5.5's 58.6%)
1M token context via MSA (15.6× faster than standard attention)
Native text + images + video input
Computer use (desktop operation)
$0.60/$2.40 per million tokens
Weights dropping ~June 10

It reproduced an ICLR 2025 paper autonomously — 12 hours, 18 commits, 23 figures, zero human intervention.

My take: M3 is the model that should worry Anthropic most. It beats GPT-5.5 on coding while being open-weight, multimodal, and 10× cheaper than Opus. When weights drop (~June 10), enterprises with data privacy requirements get a frontier model they can run on-premise. That was previously impossible without Claude's closed API. See our M3 complete guide, vs Claude Opus 4.8, and vs DeepSeek V4-Pro.

5. Grok's next model teased (mid-June)

xAI teased their next Grok model — reportedly tripling the parameter count with a focus on coding leadership. Expected release: mid-June 2026. Supervised fine-tuning was complete as of the announcement, with reinforcement learning underway.

If it delivers on the coding promise, it could shake up the Grok Build ecosystem significantly. Currently Grok Build uses Grok 4.3 — an upgrade to whatever this new model is could make the $30/mo subscription much more competitive.

6. Quick hits

OpenAI Codex on Amazon Bedrock — GPT models + Codex now generally available on AWS with enterprise controls. OpenAI meeting customers where they already deploy.
White House signed frontier-model cyber order — Regulation incoming for the most capable AI models used in cybersecurity applications.
Anthropic expanded Project Glasswing — More organizations getting access to Mythos Preview for cybersecurity work. Broader release "in weeks."
NVIDIA Nemotron 3 Ultra free on OpenRouter — NVIDIA's own model now available at zero cost via OpenRouter.

What I'm watching next week

MiniMax M3 weights (~June 10) — Will they live up to the benchmarks when the community tests them?
Grok new model (mid-June) — Does tripling parameters actually improve coding?
Gemini CLI sunset (June 18) — Two weeks left. Migrate now if you haven't.
Anthropic Mythos — Still "coming in weeks." Will it ship before the Grok model does?
Our race agents — Xiaomi hit session 456. 605 users/week on its site. Zero revenue still. 5 weeks left. Follow along →

AI Dev Weekly publishes every Thursday. Subscribe for weekly race updates and AI developer news.

Originally published at https://www.aimadetools.com

AI and GDPR — What Developers Actually Need to Know (2026)

Joske Vermeulen — Tue, 02 Jun 2026 12:46:18 +0000

If you're a developer at an EU company using Claude Code, Cursor, or any AI coding tool — your company may be violating GDPR without knowing it. Every prompt you send is data that gets processed on someone else's servers.

Here's what you actually need to know.

The core problem

When you use an AI coding tool, your code travels to external servers. If that code contains:

Personal data (user emails, names, addresses in test fixtures)
Database schemas with PII fields
API keys or credentials
Customer data in config files

...then you're transferring personal data to a third-party processor. Under GDPR, that requires a legal basis, a Data Processing Agreement (DPA), and potentially a Transfer Impact Assessment if the data leaves the EU.

Which AI tools are GDPR compliant?

Tool/Provider	DPA available?	EU data residency?	Training on your data?
Mistral API	✅ Yes	✅ EU-based	❌ No
Anthropic API (Claude)	✅ Yes (Team/Enterprise)	⚠️ US servers	❌ No (API)
OpenAI API	✅ Yes	⚠️ US servers (EU option available)	❌ No (API)
Google Vertex AI	✅ Yes	✅ EU region available	❌ No
Claude Pro subscription	❌ Consumer terms	❌ US	⚠️ May be used
ChatGPT Plus	❌ Consumer terms	❌ US	⚠️ May be used
Self-hosted	N/A (your servers)	✅ You control it	❌ No

Key distinction: API access (business terms, DPA available) is different from consumer subscriptions (personal terms, no DPA). If your company uses ChatGPT Plus or Claude Pro for work, that's a compliance risk.

The safest options for EU developers

Option 1: Self-hosted (zero data transfer)

Run models locally — nothing leaves your machine:

ollama pull devstral-small:24b
aider --model ollama/devstral-small:24b

See our self-hosted AI guide and Ollama guide.

Option 2: Mistral API (EU-native)

Mistral is based in Paris. Data stays in the EU by default. No transatlantic transfers, no Standard Contractual Clauses needed.

from mistralai import Mistral
client = Mistral(api_key="your-key")
# Data processed in EU infrastructure

See our Mistral API guide.

Option 3: US providers with DPA + EU region

Anthropic and OpenAI offer business plans with DPAs. Google Vertex AI lets you specify EU regions. This is compliant but requires paperwork.

What about AI coding tools?

Tool	GDPR-safe?	Why
Aider + local model	✅	Nothing leaves your machine
Continue.dev + Ollama	✅	Local inference
Aider + Mistral API	✅	EU data residency
Claude Code (Pro sub)	❌	Consumer terms, US servers
Cursor	⚠️	Business plan has DPA
GitHub Copilot Business	✅	DPA + no training on code

Practical steps

Audit your AI tools — list every AI service your team uses
Check for DPAs — consumer subscriptions don't count
Scrub test data — remove real PII from test fixtures and seed data
Consider self-hosting for sensitive codebases
Use Mistral as your default EU-compliant provider
Document everything — GDPR requires you to demonstrate compliance

FAQ

Do AI tools comply with GDPR?

It depends on the tool and plan. API-based services (OpenAI API, Anthropic API, Google Vertex AI) offer Data Processing Agreements and don't train on your data. Consumer subscriptions like ChatGPT Plus or Claude Pro use personal terms without DPAs and may not be GDPR-compliant for business use.

Can I use ChatGPT for GDPR-regulated data?

You can use the OpenAI API with a business agreement and DPA in place, but not the consumer ChatGPT Plus subscription. The API doesn't train on your data and offers EU data residency options, making it suitable for regulated workloads with proper legal agreements.

Do I need a DPA for AI APIs?

Yes, if you're sending any personal data to the API. Under GDPR, any third-party processing personal data on your behalf requires a Data Processing Agreement. Most major AI providers (OpenAI, Anthropic, Google) offer DPAs on their business and enterprise plans.

Is self-hosted AI GDPR compliant?

Self-hosted AI eliminates data transfer concerns since nothing leaves your infrastructure. However, you still need to comply with other GDPR requirements like data minimization, purpose limitation, and the right to erasure for any personal data the model processes or stores.

Related: Where Does Your Code Go? · Self-Hosted AI for GDPR · EU AI Act for Developers · Best AI Coding Agents for Privacy · Best VPNs for Developers · Uk Ai Regulation After Brexit · Ccpa Ai Developers

Originally published at https://www.aimadetools.com

AI Dev Weekly #12: Opus 4.8 Drops, Anthropic Hits $965B, Chinese AI Goes 99% Cheaper, Microsoft Builds Its Own Coding Model

Joske Vermeulen — Fri, 29 May 2026 07:09:56 +0000

AI Dev Weekly is a Thursday series where I cover the week's most important AI developer news, with my take as someone who actually uses these tools daily.

The theme this week is divergence. US labs are raising prices and valuations. Chinese labs are racing to zero. Developers are caught in the middle choosing between the absolute best (Opus 4.8 at $25/M output) and "good enough at 3% of the cost" (DeepSeek/MiMo at $0.87/M). Meanwhile Microsoft is hedging by building its own coding model to reduce OpenAI dependency. Let's break it all down.

1. Claude Opus 4.8: the new #1 coding model

Anthropic released Claude Opus 4.8 on May 28. Same price as 4.7 ($5/$25 per million tokens), better at everything.

Key numbers:

69.2% SWE-bench Pro — up from 64.3% (4.7) and miles ahead of GPT-5.5 (58.6%)
74.2% Terminal-Bench 2.1 — +8.4 points over 4.7
88.6% SWE-bench Verified — highest of any model
4× fewer unflagged code flaws — the honesty improvement is the real story
61.4 Artificial Analysis Index — takes #1 from GPT-5.5

The biggest new feature is dynamic workflows in Claude Code. Claude can now plan a large task, spawn hundreds of parallel subagents, verify results, and iterate until convergence. Jarred Sumner used it to port Bun from Zig to Rust — 750,000 lines, 11 days, 99.8% test pass rate.

Other additions: effort control (low → max), fast mode at 3× cheaper ($10/$50 instead of $30/$150), and system messages mid-conversation in the API.

My take: I run Claude as one of seven agents in the $100 AI Startup Race. The 4.8 vs 4.7 improvement is immediately noticeable — fewer hallucinated progress claims, better self-correction, more efficient tool calling. The dynamic workflows feature is genuinely new territory. No other tool can spawn hundreds of coordinated agents from a single prompt. For codebase-scale migrations and audits, this is a step change. The question is whether the $25/M output price is justified when DeepSeek scores within 8 points for $0.87/M.

2. Anthropic raises $65B, surpasses OpenAI at $965B

Alongside the Opus 4.8 launch, Anthropic closed a $65 billion Series H at a $965 billion post-money valuation. That puts them above OpenAI for the first time.

The numbers:

$965B valuation (OpenAI was last valued at ~$900B)
$47B annualized revenue run rate — tripled in 3 months
Led by Altimeter Capital, Dragoneer, Greenoaks, Sequoia Capital
Mythos-class models (higher intelligence than Opus) coming "in weeks"

My take: The valuation flip is symbolic but the revenue growth is real. $47B run rate means Claude is generating serious enterprise revenue. The Mythos tease is interesting — they explicitly said it has "even higher intelligence than Opus" and is currently limited to cybersecurity work under Project Glasswing. If Mythos ships broadly in June, it could be another step change. For developers, the practical implication is: Anthropic has the resources to keep shipping fast. Expect monthly model updates to continue.

3. The Chinese AI pricing war goes nuclear

Two massive price cuts in one week made Chinese frontier models essentially free for cached workloads:

DeepSeek V4-Pro (May 22): The 75% promotional discount is now permanent. Output locked at $0.87/M tokens. Input at $0.435/M. Cache hits at $0.003625/M. This is a model that scores 80.6% on SWE-bench Verified — within 8 points of Opus 4.8.

MiMo V2.5 Pro (May 26): Xiaomi cut prices by up to 99%. Cached input dropped from $0.36/M to $0.0036/M. Standard pricing now matches DeepSeek exactly: $0.435/$0.87. Token Plans upgraded 5-51× (the $100 plan now gets 82 billion tokens).

The technical explanation: both labs achieved architectural breakthroughs in KV cache efficiency. DeepSeek's interleaved attention reduces cache to 10% of standard size. MiMo's hierarchical SWA uses a 1:7 sparsity ratio. Both claim break-even at these prices.

The result: Chinese AI models are now 30× cheaper than American equivalents on standard pricing, and 100×+ cheaper on cached workloads. For agent pipelines with stable system prompts, the effective cost is approaching zero.

My take: I tripled the Xiaomi agent's sessions in our race (from 2 to 6 per day) because the cost became negligible. At $0.0036/M cached tokens, running an autonomous agent 24/7 costs less than a cup of coffee per day. The quality gap is real but narrowing — DeepSeek V4-Pro at 80.6% SWE-bench vs Opus 4.8 at 88.6% is meaningful for hard tasks but irrelevant for 80% of routine coding. If you are spending more than $500/month on API calls and haven't tested Chinese models, you are leaving money on the table. We wrote a full migration guide if you want to try.

4. Microsoft building its own coding model for Build 2026

Reuters reported on May 28 that Microsoft will unveil a homegrown coding model at Build 2026 (June 2-3 in San Francisco). It is designed to boost GitHub Copilot and reduce dependency on OpenAI.

Also coming at Build:

Transcription model
Reasoning model
Speech model
Image model

This is part of a broader strategic shift. Microsoft is building a self-sufficient AI stack alongside its OpenAI partnership, not instead of it. The competitive pressure from Claude Code (which has been eating Copilot's market share) is the likely catalyst.

My take: This is the most significant signal yet that the OpenAI-Microsoft relationship is evolving. Microsoft investing billions in OpenAI while simultaneously building competing models tells you everything about where the industry is heading: no one wants to be dependent on a single provider. For developers using Copilot, this could mean better performance (a model optimized specifically for code completion rather than general-purpose) or it could mean fragmentation (yet another model to evaluate). Watch Build next week for details.

5. StepFun Step 3.7 Flash: 198B MoE at 400 tokens/sec

A new player entered the cheap-and-fast model tier. StepFun released Step 3.7 Flash — a 198B parameter MoE model that activates only 11B parameters per token.

The specs:

400 tokens/second — 2× faster than Gemini 3.5 Flash
256K context window
Native multimodal — text, images, video
3 reasoning tiers — Low/Medium/High per API call
Advisor Mode — achieves 97% of Opus 4.6 coding at $0.19/task
Open-weight — self-hostable on 128GB RAM
~$0.20/M input, ~$0.80/M output on OpenRouter

The unique feature is Advisor Mode: Step 3.7 Flash handles routine execution autonomously and only escalates to a stronger model when genuinely stuck. This automated routing achieves near-frontier quality at budget prices.

My take: The "Flash" model tier is getting crowded — Gemini 3.5 Flash, Step 3.7 Flash, DeepSeek V4 Flash. All under $1/M output, all fast enough for real-time use, all surprisingly capable. Step 3.7 Flash's video understanding and GUI interaction capabilities set it apart. The 400 t/s throughput is genuinely impressive for a model this capable. If you need speed + multimodal + cheap, this is worth testing.

6. Quick hits

OpenAI reportedly dropped GPT-5.3 Codex minutes after Anthropic's Opus 4.8 announcement. 25% faster than GPT-5.2, reportedly helped debug itself during development. Supercharges the Codex agentic coding tool launched earlier this month.
Cohere acquired Aleph Alpha — creating a $20B transatlantic sovereign AI company. Backed by Schwarz Group (Europe's largest retailer). Positions as the enterprise alternative to US/Chinese models for European companies with data sovereignty requirements.
Canada ruled OpenAI violated privacy laws — regulatory pressure continues to mount on US AI labs internationally.
Claude Code shipped detailed usage analytics — you can now see exactly how many tokens each session consumed, broken down by model and effort level.

What I'm watching next week

Microsoft Build (June 2-3) — The new coding model reveal. Will it compete with Claude Code or just improve Copilot's autocomplete?
Mythos timeline — Anthropic said "coming weeks." If it ships in early June, it could leapfrog Opus 4.8 immediately.
Gemini CLI shutdown (June 18) — Two weeks until the deadline. If you haven't migrated to Antigravity CLI, time is running out.
Our race agents — Claude is at 194 blog posts. Xiaomi just got tripled to 6 sessions/day. Gemini is back online after a 4-day auth outage. Follow along →

AI Dev Weekly publishes every Thursday. Subscribe for weekly race updates and AI developer news.

Originally published at https://www.aimadetools.com

Aider Complete Guide: Setup, Best Models, and Tips (2026)

Joske Vermeulen — Wed, 27 May 2026 12:24:08 +0000

Aider is a free, open-source AI pair programming tool that runs in your terminal. You chat with it in plain English, and it directly edits files in your local Git repository. Every change is automatically committed with a descriptive message.

It's the most model-flexible AI coding tool available — it works with Claude, GPT, Gemini, DeepSeek, Qwen, local models via Ollama, or any OpenAI-compatible API.

Why Aider?

Most AI coding tools lock you into one model. Claude Code only works with Claude. Codex CLI only works with GPT. Aider works with everything.

Key strengths:

Git-native — every AI edit is a clean Git commit you can review, revert, or cherry-pick
100+ model support — any model, any provider, including local
Repo map — understands your entire codebase structure, not just open files
Multi-file editing — coordinates changes across multiple files in one operation
100+ languages — not just Python and JavaScript

Installation

pip install aider-chat

Or with pipx (recommended for isolation):

pipx install aider-chat

Quick start

cd your-project
export ANTHROPIC_API_KEY="your-key"  # or OPENAI_API_KEY, etc.
aider

Aider starts in your project directory, scans the Git repo, and builds a map of your codebase.

Choosing a model

Aider supports tiered model usage — a powerful model for complex edits and a cheaper one for simple tasks:

# Use Claude Opus for main editing, Sonnet for quick tasks
aider --model claude-3-opus-20240229 --weak-model claude-3-sonnet-20240229

# Use GPT-5.4 
aider --model gpt-5.4

# Use DeepSeek (cheapest frontier option)
aider --model deepseek/deepseek-chat

# Use a local model via Ollama
aider --model ollama/qwen3.5:27b

# Use any model via OpenRouter
export OPENROUTER_API_KEY="your-key"
aider --model openrouter/anthropic/claude-opus-4.6

See our OpenRouter guide for the full model catalog.

Core commands

Adding files to context

/add src/auth.ts src/middleware.ts
/add src/routes/*.ts          # glob patterns work
/read docs/API.md             # read-only context (not edited)
/drop src/auth.ts             # remove from context

Editing modes

Aider has multiple edit formats optimized for different models:

whole — replaces entire files (best for smaller files)
diff — sends unified diffs (best for large files)
udiff — universal diff format
editor — opens your editor for manual review

aider --edit-format diff  # Use diff mode

Git integration

/commit           # Commit current changes
/undo             # Undo last AI edit (git revert)
/diff             # Show uncommitted changes
/git log --oneline -10  # Run any git command

Every AI edit creates an automatic commit like:

aider: Refactor auth middleware to use JWT validation

Running commands

/run npm test              # Run tests
/run npm run lint          # Run linter
/test npm test             # Run tests and auto-fix failures

The /test command is powerful — it runs your test suite, and if tests fail, Aider automatically tries to fix the code and re-runs until tests pass.

Web content

/web https://docs.stripe.com/api/charges  # Fetch docs for context

Advanced features

Repo map

Aider builds a map of your entire repository — function signatures, class definitions, imports, and dependencies. This means it understands how files relate to each other, even files not explicitly added to the chat.

The repo map uses tree-sitter for parsing, supporting 100+ languages.

Linting integration

aider --lint-cmd "eslint --fix"  # Auto-lint after every edit
aider --auto-lint                # Use detected linter

Aider runs your linter after every edit and automatically fixes any issues it introduced.

Voice input

aider --voice  # Use speech-to-text for input

Describe changes verbally — useful for complex explanations that are easier to speak than type.

Scripting mode

echo "Add error handling to all API routes" | aider --yes --no-git

Run Aider non-interactively for CI/CD pipelines or batch operations.

Configuration

Create .aider.conf.yml in your project root:

model: claude-3-opus-20240229
weak-model: claude-3-sonnet-20240229
edit-format: diff
auto-lint: true
lint-cmd: eslint --fix
auto-commits: true

Or use environment variables:

export AIDER_MODEL=claude-3-opus-20240229
export AIDER_WEAK_MODEL=claude-3-sonnet-20240229

Cost optimization

Aider can be expensive with frontier models. Tips to reduce costs:

Use --read for context files — read-only files use fewer tokens than editable ones
Use a weak model for simple tasks — --weak-model routes simple edits to a cheaper model
Use DeepSeek or Qwen — 10-30x cheaper than Claude/GPT for comparable quality
Truncate large files — Aider sends entire files as context; keep them focused
Use local models for routine work — Ollama with Qwen 3.5 27B is free

Aider vs Claude Code vs Codex CLI

Feature	Aider	Claude Code	Codex CLI
Model support	Any model	Claude only	GPT only
Git integration	Deep (auto-commits)	Basic	Basic
Repo map	✅ Full codebase	✅ Full codebase	✅ Full codebase
Price	Free (BYOK)	$20/mo sub	$20/mo sub
Edit quality	Model-dependent	Excellent	Very good
Voice input	✅	❌	❌
Linting	✅ Auto-lint	❌	❌
Open source	✅ Apache 2.0	❌	✅

Choose Aider when: You want model flexibility, deep Git integration, or need to use cheap/local models.

Choose Claude Code when: You want the best code quality and don't mind being locked to Claude.

Choose Codex CLI when: You're in the OpenAI ecosystem and want fast autonomous coding.

Bottom line

Aider is the Swiss Army knife of AI coding tools. It's not the best at any single thing — Claude Code writes better code, Codex CLI is faster, Kimi CLI has Agent Swarm. But Aider is the most flexible, the most transparent (Git-native), and the only tool that lets you freely switch between any model from any provider.

For developers who want control over their AI coding workflow without vendor lock-in, Aider is the best choice in 2026.

FAQ

Is Aider free?

Yes. Aider is 100% free and open-source (Apache 2.0). You bring your own API key for whichever AI model you want to use — there's no subscription or usage fee for Aider itself.

Which AI models work with Aider?

Aider works with 100+ models including Claude, GPT, Gemini, DeepSeek, Qwen, Mistral, and any OpenAI-compatible API. See our guide to using Aider with DeepSeek for a budget-friendly setup.

How does Aider compare to Claude Code?

Aider supports any model while Claude Code is locked to Claude. Aider has deeper Git integration (auto-commits every edit) and auto-linting. Claude Code produces slightly better code quality since it's optimized for one model. See our full Aider vs Claude Code vs Codex comparison.

Can I use Aider with local models?

Yes. Aider works with local models via Ollama or any OpenAI-compatible local server. Run aider --model ollama/qwen3.5:27b to use a local model at zero cost.

Does Aider work with Git?

Git integration is Aider's core feature. Every AI edit is automatically committed with a descriptive message. You can review, revert, or cherry-pick any change. Aider also builds a repo map from your Git repository to understand your full codebase.

Related: Aider vs Claude Code vs Codex CLI · Best AI Coding Tools 2026 · Ollama Complete Guide

Originally published at https://www.aimadetools.com