DeepSeek V4 Pro 0813 (2026): Complete Guide to Pricing, Benchmarks & How It Stacks Up Against Fable 5
🎯 Core Takeaways (TL;DR)
-
DeepSeek V4 Pro 0813 is the general-availability (GA) release of DeepSeek's 1.6T-parameter MoE flagship, live on August 12, 2026 — the official
deepseek-v4-proAPI endpoint now points to this build. - The headline story is agentic coding: DeepSWE jumped from 12.8 to 62.7, CyberGym from 52.7 to 83.3, and Terminal Bench 2.1 from 72.1 to 87.9 versus the preview — near Fable 5 territory at roughly 1/60th of the price.
- Pricing is $0.435 per million input tokens (cache miss), $0.003625 (cache hit), and $0.87 per million output tokens, with a 1M-token context window — though DeepSeek has announced a significant price increase is coming.
- DeepSeek V4 Pro 0813 has no vision support, a quirk that split the community — some see it as a dealbreaker, others as a reasonable trade for frontier-adjacent performance at budget pricing.
Table of Contents
- What Is DeepSeek V4 Pro 0813?
- DeepSeek V4 Pro 0813 Pricing Breakdown
- Benchmarks: The Big Agentic Gains
- DeepSeek V4 Pro 0813 vs Fable 5 vs Opus 4.8
- Real-World Feedback: What Early Users Report
- How to Access DeepSeek V4 Pro 0813
- FAQ
What Is DeepSeek V4 Pro 0813?
DeepSeek V4 Pro 0813 is the general-availability build of DeepSeek V4 Pro, DeepSeek's large-scale mixture-of-experts (MoE) model. The "0813" suffix marks the August 13 build identifier that closed out a preview period stretching back to the V4 family's open-weight debut on April 24, 2026.
Under the hood, DeepSeek V4 Pro 0813 is a massive model: 1.6 trillion total parameters with roughly 49 billion active per token, pretrained on more than 32 trillion tokens. It uses a hybrid attention system designed to cut inference costs at long context lengths — which matters, because the model supports a 1,048,576-token (1M) context window and up to 384,000 output tokens.
The release was staged deliberately. The smaller DeepSeek V4 Flash graduated to official status first on July 31, 2026, with DeepSeek's changelog promising the Pro release "will follow soon." That promise materialized on August 12, when the deepseek-v4-pro endpoint on DeepSeek's official API silently began pointing at DeepSeek V4 Pro 0813. Notably, as Simon Willison pointed out on Hacker News, DeepSeek shipped this upgrade without a blog post or even a tweet — the only announcement was the API documentation update itself.
💡 Professional Tip: Because DeepSeek V4 Pro 0813 shipped without an official announcement page, the most reliable places to track it are the DeepSeek API pricing page and its OpenRouter model page.
DeepSeek V4 Pro 0813 Pricing Breakdown
For an open-weight model of this size, DeepSeek V4 Pro 0813 is priced aggressively:
| Pricing Metric | DeepSeek V4 Pro 0813 | DeepSeek V4 Flash 0731 |
|---|---|---|
| Input (cache miss) / 1M tokens | $0.435 | $0.14 |
| Input (cache hit) / 1M tokens | $0.003625 | $0.0028 |
| Output / 1M tokens | $0.87 | $0.28 |
| Context window | 1M | 1M |
| Max output | 384K | 384K |
| Concurrency limit | 500 | 2,500 |
The cache-hit price is the sleeper stat. On OpenRouter, the measured cache hit rate for DeepSeek V4 Pro 0813 averages 88.9–92.17%, which drags the weighted-average input price customers actually pay down to about $0.051 per million tokens — roughly 1/8 of the listed rate. One HN user ran a heavy agentic workload (traffic simulator and distributed physics engine) all day for about $12.50, and commenters noted that with a properly configured harness pushing cache hits toward 99%, the same workload would cost a few dollars.
Two caveats worth knowing before you build on DeepSeek V4 Pro 0813:
⚠️ Attention: DeepSeek's official pricing page states it "plans to raise the overall pricing for DeepSeek API services in the near future, with a significant increase expected." Budget accordingly.
⚠️ Attention: Until more providers come online, OpenRouter routes DeepSeek V4 Pro 0813 directly through DeepSeek's official API, which may log and train on your prompts. Check DeepSeek's data policies before sending sensitive code.
Benchmarks: The Big Agentic Gains
The reason DeepSeek V4 Pro 0813 dominated Hacker News and r/LocalLLaMA on release day is the sheer size of the post-training jump. DeepSeek's official numbers (shared via its WeChat group and reposted by OpenRouter) show gains that would normally take a full model generation:
| Benchmark | V4 Pro 0813 | V4 Pro Preview | Gain |
|---|---|---|---|
| DeepSWE | 62.7 | 12.8 | +49.9 |
| CyberGym | 83.3 | 52.7 | +30.6 |
| NL2Repo | 61.5 | 38.5 | +23.0 |
| Terminal Bench 2.1 | 87.9 | 72.1 | +15.8 |
| Toolathlon-Verified | 74.1 | 55.9 | +18.2 |
| AutomationBench (Public) | 31.8 | 12.8 | +19.0 |
| DSBench-FullStack | 71.1 | 41.8 | +29.3 |
| DSBench-Hard | 67.2 | 31.1 | +36.1 |
The DeepSWE number deserves a double take. Across two releases, DeepSeek's engineering benchmark score went 7.3 → 12.8 → 62.7. As one Redditor put it: "The jump on DeepSWE is probably the most interesting number here. 7.3 → 62.7 in two releases is kind of wild 😅". DeepSeek attributes these gains entirely to improved post-training — the architecture of DeepSeek V4 Pro 0813 is unchanged from the preview.
DeepSeek V4 Pro 0813 vs Fable 5 vs Opus 4.8
The comparison that set the community buzzing: DeepSeek V4 Pro 0813 lands within a few points of Anthropic's frontier models on agentic benchmarks, at a fraction of the cost.
| Benchmark | DeepSeek V4 Pro 0813 | Opus 4.8 | Fable 5 (w/ fallback) | Kimi K3 | GLM-5.2 |
|---|---|---|---|---|---|
| Terminal Bench 2.1 | 87.9 | 85.0 | 88.0 | 88.3 | 81.0 |
| Cybergym | 83.3 | 78.3 | 83.1 | 80.0 | – |
| DeepSWE | 62.7 | 58.0 | 70.0 | 67.5 | 46.2 |
| Toolathlon-Verified | 74.1 | 76.2 | 77.9 | 76.5 | 59.9 |
| HLE (with tools) | 60.0 | 57.9 | 63.0 | 56.0 | 54.7 |
| AutomationBench (Public) | 31.8 | 27.2 | 29.1 | 30.8 | 12.9 |
| DSBench-Hard | 67.2 | 71.7 | 68.3 | 63.0 | 54.5 |
One HN user computed geometric means across these suites: GPT-5.6 Sol leads at 65.5, Fable 5 at 64.5, Opus 5 at 64.0 — and DeepSeek V4 Pro 0813 at 62.5, ahead of Kimi K3's 62.3. That puts a $0.435/$0.87 model inside ~2 points of the most expensive models on the market. As r/LocalLLaMA commenters put it: "Close to Fable, nice. Comparing the pricing — nicer." and "this model is $0.87 per million tokens folks... basically 1/60th of Fable's cost."
✅ Best Practice: If your workload is text-only agentic coding, DeepSeek V4 Pro 0813 is the value play. If you need multimodal input, situational awareness for safety-sensitive reasoning, or maximum single-task reliability, a frontier model like Fable 5 or Opus still justifies its premium.
Real-World Feedback: What Early Users Report
Vibes on DeepSeek V4 Pro 0813 are mostly positive, with two recurring criticisms:
The wins:
- A traffic-simulator workload found "some pretty significant gains without introducing any new problems" (~$12.50 for 2B tokens at 50% cache hits).
- Simon Willison's SVG-rendering tests showed distinct, coherent "styles" at low/medium/high reasoning efforts — "almost like 3 models in one," as one commenter observed.
- Local-hosting enthusiasts report you can self-host the open weights on a >128GB VRAM + 768GB RAM rig built from used P40s for roughly $3–3.5K.
The criticisms:
- One HN test on Codex CLI: DeepSeek V4 Pro 0813 took 12m02s and $0.12 to add a feature but shipped with a bug, while Grok 4.6 took 3m18s and $1.41 with no bug. n=1, but it matches the general pattern: cheaper and slower, usually good enough.
- Chinese developer karminski3 reported that with
reasoning_effort=maxon long-horizon agentic coding, the model tends to stop early: in a 50-round optimization loop, 2 of 3 runs halted around round 42–43 without using all available attempts, and it didn't beat GLM-5.1 in that setup. If you run long autonomous loops on DeepSeek V4 Pro 0813, monitor for premature termination. - No vision. DeepSeek reportedly doesn't consider vision part of its AGI path, so it's not on the roadmap — the single biggest feature complaint on both Hacker News and Reddit.
How to Access DeepSeek V4 Pro 0813
graph TD
A[Choose an access route] --> B[DeepSeek official API<br/>model: deepseek-v4-pro]
A --> C[OpenRouter<br/>model: deepseek/deepseek-v4-pro-0813]
B --> D[Enable thinking mode + tool calls]
C --> D
D --> E[Maximize cache hits<br/>never rewrite conversation history]
E --> F[Monitor long agent loops<br/>for early stopping]
You can reach DeepSeek V4 Pro 0813 today through:
-
DeepSeek's official API — the
deepseek-v4-proendpoint now serves the 0813 build; OpenAI-format, Anthropic-format, Responses API, JSON output, and tool calls are all supported. -
OpenRouter —
deepseek/deepseek-v4-pro-0813, currently hosted by a single provider (DeepSeek) with 100% uptime over the last three days, ~61 tokens/s throughput, and ~1.56s P50 latency. Early traffic shows Hermes Agent, pi, and Claude Code among the top apps already routing to it. - Self-hosting — the MIT-licensed open weights mean you can run it on your own hardware if you have the VRAM/RAM budget.
🤔 FAQ: DeepSeek V4 Pro 0813
Q: What is DeepSeek V4 Pro 0813?
A: DeepSeek V4 Pro 0813 is the general-availability (GA) release of DeepSeek V4 Pro, a 1.6T-parameter mixture-of-experts model with ~49B active parameters, a 1M-token context window, and up to 384K output tokens, released on August 12, 2026.
Q: How much does DeepSeek V4 Pro 0813 cost?
A: DeepSeek V4 Pro 0813 costs $0.435 per million input tokens on a cache miss, $0.003625 per million on a cache hit, and $0.87 per million output tokens. DeepSeek has announced a significant price increase is planned in the near future.
Q: What is the context length of DeepSeek V4 Pro 0813?
A: DeepSeek V4 Pro 0813 supports a 1,048,576-token (1M) context window with a maximum output of 384,000 tokens — enough for full-codebase analysis and long-horizon agent sessions.
Q: Does DeepSeek V4 Pro 0813 support tool calling and structured outputs?
A: Yes. DeepSeek V4 Pro 0813 supports tool calls, JSON output, the Responses API, both OpenAI and Anthropic API formats, and thinking/non-thinking modes. Reasoning efforts high and xhigh are supported, with xhigh mapping to max reasoning.
Q: Does DeepSeek V4 Pro 0813 support vision?
A: No. DeepSeek V4 Pro 0813 is text-only. DeepSeek has signaled that vision isn't on the roadmap for the V4 Pro line, which is the most common complaint about the model on Hacker News and r/LocalLLaMA.
Q: When was DeepSeek V4 Pro 0813 released?
A: DeepSeek V4 Pro 0813 went live on August 12, 2026, when DeepSeek's official deepseek-v4-pro API endpoint switched from the April preview build to the 0813 build. It appeared on OpenRouter the same day.
Q: Is DeepSeek V4 Pro 0813 open source?
A: Yes. The V4 series (Pro and Flash) was released as MIT-licensed open weights on April 24, 2026, and DeepSeek V4 Pro 0813 retains that license, so you can self-host and commercialize freely.
Summary & Recommended Actions
DeepSeek V4 Pro 0813 is the clearest proof yet that open-weight models have closed the agentic-coding gap to the frontier — 62.7 on DeepSWE, 87.9 on Terminal Bench 2.1, and a geometric mean within ~2 points of Fable 5, at roughly 1/60th of the price. It's not the best model in the world: it lacks vision, it's slower than Grok-class models, and at least one long-loop test found premature early stopping. But for text-only coding agents, batch work, and cost-sensitive pipelines, it resets the value equation.
Your next steps:
- Try it for $1: spin up a test on OpenRouter with a real coding task and compare against your current model.
- Optimize for cache hits: keep system prompts and conversation history byte-stable — at $0.003625/M, cache hits are where DeepSeek V4 Pro 0813 gets absurdly cheap.
- Hedge the price increase: DeepSeek has flagged a "significant" API price hike; test DeepSeek V4 Pro 0813 now while the preview-era pricing lasts, and keep Flash 0731 in your back pocket for high-volume workloads.
Sources: OpenRouter model page & FAQ · DeepSeek API pricing docs · Hacker News discussion · r/LocalLLaMA benchmark thread · OpenRouter announcement on X · karminski3 agent-loop test · GMICloud release analysis
Originally published at: DeepSeek V4 Pro 0813 (2026): Complete Guide to Pricing, Benchmarks & How It Stacks Up Against Fable 5
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