Originally published on dreaming.press.
The short version: the ceiling went up, one cache-read price fell through the floor, and two of this quarter's cheap numbers expire on January 1. If you ship on an LLM API, three things changed since August's table: OpenAI's GPT-6 Astra set a new ~$10/$50-per-million ceiling, Anthropic's Fable 5.1 cut cache reads 75% to $0.25/1M, and Gemini 3.8 Flash arrived cheap but is priced to double on Jan 1, 2027. Here is the whole table in one screen, then the formula that turns it into a bill, then the two moves worth making this week.
The table (per 1M tokens, standard context, early September 2026)
Model Input Output Cache read Context Best for
GPT-6 Astra $10 $50 ~$1 ~1.05M The new ceiling — hardest autonomous work (gated preview)
Claude Fable 5.1 $10 $50 $0.25 1M Agentic coding where cache reads dominate the bill
Claude Opus 5 $5 $25 $0.50 1M Hard reasoning, long-horizon agents
GPT-5.6 Sol $5 $30 ~$0.50 ~1M OpenAI flagship reasoning
Claude Sonnet 5 $3 $15 $0.30 1M Everyday workhorse
Gemini 3.1 Pro $2 $12 ~$0.20 1M Long-context, multimodal
GPT-5.6 Terra $2 $12 ~$0.20 ~1M Mid-tier general work
Claude Haiku 4.5 $1 $5 $0.10 200K Cheap, fast classification/extraction
Gemini 3.8 Flash $0.75 $3.75 ~$0.08 ~1M Cheap coding + agents — intro, doubles Jan 1
GPT-5.6 Luna $0.20 $1.20 ~$0.02 ~1M High-volume budget calls
DeepSeek V4-Pro $0.66 $1.98 $0.022 128K Cheap frontier-ish, off-peak
DeepSeek V4-Flash $0.22 $0.66 $0.007 128K Cheapest credible tier, off-peak
Prices move weekly, and several here are introductory or gated. Confirm the current number on the provider's own pricing page before you commit a budget to it — this table is a map, not a quote.
The one formula that turns tokens into a bill
Nothing about the arithmetic changed, so if you've read the August edition you can skim this. Price input and output separately , because on every frontier model output costs 4-8x more per token than input:
monthly cost = (avg input tokens × input price + avg output tokens × output price) × requests per month
A worked example on Claude Sonnet 5 ($3 input / $15 output per 1M), sending ~1,500 input and generating ~500 output tokens per request at 100,000 requests a month:
(1,500 × $3/1M + 500 × $15/1M) × 100,000 = ($0.0045 + $0.0075) × 100,000 ≈ $1,200/month before discounts.
Two rules keep the estimate honest: count output at its higher rate (it usually dominates for chat), and apply a 1.7-2x buffer for retries, system prompts, and tool overhead you'll forget to count. Then subtract caching and batching — which, this month more than ever, is where the real number lands.
What actually changed in September
1. A new ceiling: GPT-6 Astra at ~$10/$50
OpenAI began rolling out GPT-6 Astra on Sept 3 as an application-gated preview — the first model it has ever rated "Critical" for cyber capability under its Preparedness Framework. Reported API list pricing is about $10 per 1M input and $50 per 1M output (cached input ~$1, batch ~half, a Fast mode at ~2x), with a ~1.05M-token context. That's double the prior flagship tier. The signal for a founder: the top of the market got more expensive, so don't assume "the newest model" is a cost upgrade — Astra is a capability ceiling to prototype against, not a default to route production traffic to.
2. The cache-read war: Fable 5.1 cuts reads 75%
On Sept 1, Anthropic shipped Claude Fable 5.1 with base rates unchanged ($10/M in, $50/M out) but cache reads cut 75%, from $1.00 to $0.25 per 1M input . Anthropic says that lowers real cost ~25% on typical workloads and up to ~45% on highly agentic ones — with nothing to change but the model string. This is the most important number in the table for anyone running agents, because an agent's bill is 70-85% input, and most of that input is a resent prefix that cache reads discount. At $0.25 against a $10 base, Fable reads your stable context at 2.5% of the list input rate. If you run long system prompts, RAG, or agent loops, re-run your heaviest workload on Fable 5.1 and re-measure cost per completed task , not cost per token.
3. The promo cliff: two cheap numbers reset on Jan 1
Google's Gemini 3.8 Flash launched Sept 2 at $0.75/$3.75 per 1M — genuinely cheap for a coding-and-agents workhorse — but that is introductory through Dec 31, 2026, doubling to $1.50/$7.50 on Jan 1, 2027 . Microsoft's MAI-Transcribe-2 ($0.10/hour of audio) carries the same year-end reset. If you standardize on either this quarter, put the post-promo price in your 2027 forecast now. The workhorse that pencils out today at $0.75 costs $1.50 in four months, and a margin model built on the sticker breaks exactly when your usage has grown enough to hurt.
The two moves worth making this week
- If you run agents, test Fable 5.1's cache reads. The 75% cut is aimed squarely at the resent-prefix cost that dominates agent bills. Point your heaviest agent loop at it and measure cost-per-completed-task against your current model. This is the cheapest cost cut available this month — a model-string change, not a rewrite.
- Re-price your 2027 on post-promo numbers. Any budget model with a Jan 1 reset (Gemini 3.8 Flash, MAI-Transcribe-2) should appear in your forecast at its doubled rate. Keep the pipeline provider-swappable so that when the promo ends you can route away in an afternoon, not a sprint.
The through-line hasn't changed since August: the sticker price is the least interesting number on the page. Your output-to-input ratio, your cache-hit rate, and whether your cheapest model survives its own promo period decide the bill. For the workload-by-workload picks underneath these prices, pair this with the agent model price map; for where to serve open weights instead, the serverless-inference comparison is the companion piece.
Every price here is a list rate as reported in early September 2026 and linked to the provider's pricing page plus at least one independent cross-check. GPT-6 Astra pricing is reported for a gated preview and may change at general availability; DeepSeek rates are off-peak standard and roughly double during peak UTC windows. Confirm the live number before committing a budget.
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