Hosted preview launched October 6, 2026 on Mistral's API and Mistral Studio; open weights scheduled for late October. Gateway id mistral/mistral-large-4. $0.68/$2.09 per 1M. 524,288 context, 262,144 max output; text and image input. Mixture-of-experts, roughly 1 trillion total parameters (reports of active parameters vary from 49B to 52B). Vals AI index: 48.05%, 32nd of 44. Verified October 10, 2026.
$0.68/$2.09 per 1M — the cheapest flagship from a US or European lab
Open weights scheduled for release, so it can move in-house later
512K context with up to 256K output
Weaknesses
Still a preview: the weights were not downloadable as of October 10, 2026
All benchmark claims so far are Mistral's own; Vals AI ranks it 32nd of 44 on its index
Real-world use cases
What people actually use Mistral Large 4 for.
Teams that need a European provider or plan to self-host once the weights ship
Finance and cybersecurity analysis, the domains Mistral highlights
Visual grounding over charts, diagrams and screenshots
How Mistral Large 4 compares
The nearest models people weigh against it, and what actually separates them.
vs GPT-6 Astra — Against GPT-6 Astra (OpenAI), Mistral Large 4 runs about 95% cheaper per token, gives up 2x on context and answers faster. Take Mistral Large 4 unless you specifically need what GPT-6 Astra does better.
vs Claude Fable 5.1 — Against Claude Fable 5.1 (Anthropic), Mistral Large 4 runs about 95% cheaper per token, gives up 1.9x on context and answers faster. Take Mistral Large 4 unless you specifically need what Claude Fable 5.1 does better.
vs Claude Fable 5 — Against Claude Fable 5 (Anthropic), Mistral Large 4 runs about 95% cheaper per token, gives up 1.9x on context and answers faster. Take Mistral Large 4 unless you specifically need what Claude Fable 5 does better.
Ready to try it?
Start using Mistral Large 4
European-hosted and open-weight deployments that need a frontier-class model. Start free — no card required.
OpenAI's September 3, 2026 frontier release — the first GPT-6 model and OpenAI's answer to Claude Fable 5.1 two days earlier. State of the art on computer use (OSWorld 2.0 72.6% in ~47% less time than GPT-5.6 Sol), agentic coding (Terminal-Bench 4.0 57.9%), and frontier math (FrontierMath Tier 4 97.6%). $10/$50 per 1M tokens, 1.05M context, 128K output, knowledge cutoff April 30, 2026.
Verdict
OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Quality score
98%
Pricing
$10.00/1M in
$50.00/1M out
Speed
Deliberate
2/5 speed
Context
1.1M tokens
Released September 3, 2026. API ID gpt-6-astra; rolling out over the coming days to ChatGPT Plus, Pro, Business and Enterprise (usage inside existing allowances; GPT-6 Astra Pro for Pro/Business/Enterprise; Enterprise off by default), the OpenAI API, Microsoft Azure and Amazon Bedrock. Standard API pricing $10/$50 per 1M tokens; Fast mode is up to 2x speed at 2x price; cache reads and writes have separate rates. Model docs list 1,050,000 context, 128,000 max output, knowledge cutoff April 30, 2026, reasoning efforts up to 'max'. Published launch numbers (Astra / GPT-5.6 Sol / Fable 5.1 / Opus 5): OSWorld 2.0 72.6 / 65.7 / — / 70.2; Terminal-Bench 4.0 57.9 / 37.3 / 55.8 / 52.3; Terminal-Bench Science 0.1 64.6 / 22.4 / 52.6 / 30.0; FrontierMath Tier 4 v2 97.6 / 83.0 / 87.8 / 73.2; GPQA Diamond 96.0 / 94.6 / 93.7 / 93.7; Humanity's Last Exam w/ tools 57.2 / — / 65.0 / 63.6; AutomationBench 41.4 / 18.1 / 31.4 / 26.9; DeepSWE v1.1 74.1 / 72.7 / 67.4 / 73.7; ARC-AGI-2 95.0 / 92.5 / 90.0 / 90.4; ARC-AGI-3 99.9 (OpenAI responses-API harness; ARC Prize's stateless runs score far lower) / 7.8 / — / 30.2; ExploitBench 100.0 / 78.5 / — / 70; SRE-Bench 88.0 / 55.9; Artificial Analysis Intelligence Index v4.1.1 61.2 / 60.9 / 65.7 / 63.1. Meets the Critical threshold for cybersecurity under OpenAI's Preparedness Framework; advanced cyber workflows gated behind OpenAI Daybreak. All figures from OpenAI's launch post and model docs, verified September 4, 2026.
Computer use leaderFrontierAgenticReasoningLong contextPremiumNew
Best for
Computer and browser use, long-horizon agentic coding, and frontier math and science work
Anthropic's September 1, 2026 frontier release and the new capability ceiling for coding, agents, and scientific work. Base pricing is unchanged at $10/$50, but cache reads dropped 75% to $0.25/1M — roughly 25% cheaper on typical workloads and up to 45% cheaper on agentic ones. 1M context, 128K output, adaptive thinking always on.
Verdict
Anthropic's highest-ceiling model — strongest on open-ended research; Opus 5.5 now leads coding.
Quality score
98%
Pricing
$10.00/1M in
$50.00/1M out
Speed
Deliberate
2/5 speed
Context
1M tokens
Released September 1, 2026 alongside Claude Mythos 5.1, the first update to the Mythos-class line since Fable 5 on June 9. API ID claude-fable-5-1; generally available on the Claude API, Amazon Bedrock, Google Cloud, and Microsoft Foundry. Published launch numbers (Fable 5.1 / Fable 5 / Opus 5 / GPT-5.6 Sol): Terminal-Bench-Science 0.1 52.6 / 24.7 / 29.0 / 22.4; Terminal-Bench 4.0 55.8 / 42.0 / 52.3 / 37.3; CursorBench 3.2.0 73.4 / 70.5 / 70.0 / 67.2; AutomationBench 31.4 / 17.1 / 26.9 / 19.6; OSWorld 2.0 strict 41.7 / 36.1 / 39.6; Humanity's Last Exam (no tools) 60.9 / 57.8 / 56.6; GDPval-AA v2 1853 / 1723 / 1824 / 1711. GDPval-AA v2 is rescaled from the v1 numbers quoted on the Fable 5 page and is not directly comparable to them.
Anthropic's new Mythos-class flagship and the most capable coding model anyone can use — 80.3% SWE-Bench Pro, an 11-point jump over Opus 4.8. 1M context, 128K output, native parallel subagents. Released June 9, 2026.
Verdict
Superseded by Fable 5.1 — still 80.3% SWE-Bench Pro, but 5.1 is better and cheaper to run.
Quality score
97%
Pricing
$10.00/1M in
$50.00/1M out
Speed
Deliberate
2/5 speed
Context
1M tokens
Launched June 9, 2026 as the public, Mythos-class release. Available on the Claude API, Microsoft Foundry, and Google Vertex AI. Free for all users until June 22, 2026. Same underlying model as Claude Mythos 5, with safeguards that block specific high-risk cyber responses. Superseded by Claude Fable 5.1 on September 1, 2026 — same $10/$50 base pricing, cache reads cut from $1.00 to $0.25 per 1M tokens.
SWE-Bench Pro 80.3%Mythos-classParallel subagentsAgenticLong contextPremiumSuperseded
Best for
The hardest coding tasks, autonomous multi-step agents, and frontier-grade reasoning
Pricing moves, ranking shifts, and capability updates.
New ModelOct 6, 2026
Mistral Large 4 enters public preview — open weights promised for late October
Mistral opened a hosted preview of Mistral Large 4 on October 6, 2026: a natively multimodal mixture-of-experts model with about 1 trillion total parameters, priced at $0.68/$2.09 per million tokens, with a 512K context and up to 256K output. Mistral says the open weights follow at the end of October. Every benchmark figure so far is Mistral's own; Vals AI's independent index places it 32nd of 44 models (48.05%). We have added it to the directory with that caveat. Verified October 10, 2026.
Mistral Large 4 costs $0.68 per million input tokens and $2.09 per million output tokens on the API, with cached input at $0.07 per million. A month of 10M input and 2M output tokens runs about $10.98 at list price, before any batch or caching discounts.
What is the context window of Mistral Large 4?
Mistral Large 4 has a 524k tokens context window, with up to 262k tokens of output per response. That is the total of prompt plus response the model can hold in one request.
What is Mistral Large 4 best for?
Mistral Large 4 is best for european-hosted and open-weight deployments that need a frontier-class model. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and balanced speed.
When should I avoid Mistral Large 4?
You need peak capability today — independent results so far place it mid-table — or you need the weights now.
What is a cheaper alternative to Mistral Large 4?
Claude Sonnet 5.5 (Anthropic) at $2.00/1M/1M input against Mistral Large 4's $0.68/1M/1M. Near-Opus 5.5 quality on scoped work at half the price. Compare it first if Mistral Large 4's pricing is the thing stopping you.
What is a faster alternative to Mistral Large 4?
GPT-6 Astra — deliberate against Mistral Large 4's balanced, with 1.1M tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
Newsletter
Get notified when Mistral Large 4 pricing changes
We track pricing daily. When this model drops or spikes, you'll know first.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.