TL;DR
In the Opus vs Mistral decision, Claude Opus 5.5 wins hard reasoning and coding agents, while Mistral Large 4 wins high volume and self-hosting. Opus 5.5 lists at $4 per million input tokens and $20 per million output tokens. Mistral Large 4 lists at $0.70 and $2.10, so a 100K-in, 10K-out request costs $0.091 instead of $0.60 on Opus. Both models read up to 1M tokens of context. Large 4 ships open weights under a modified MIT license but is still in public preview, while Opus 5.5 is a closed model on the Claude API and major clouds. For a mixed portfolio, route both through one gateway and decide each workload on cost per completed task.
Key Takeaways
Claude Opus 5.5, announced September 22, 2026, costs $4 input and $20 output per million tokens, 20% less than Opus 5.
Mistral Large 4 costs $0.70 input, $0.07 cached input and $2.10 output per million tokens, but it has been in public preview since October 6, 2026.
Both models accept 1M tokens of context, while Opus 5.5 also returns up to 128K output tokens.
Anthropic reports 66.4% on Terminal-Bench 4.0 for Opus 5.5, while Mistral reports 41% for the released Large 4 from a different setup.
Large 4 has about 1T total and 52B active parameters, and its open weights run on vLLM or SGLang.
At 100,000 requests a month, one sample workload costs $60,000 on Opus 5.5 compared to $9,100 on Large 4 at list price.
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A Shared-Services Team Running 100,000 Long Requests a Month
Picture a shared-services team that sends 100,000 document-heavy requests a month to an LLM. Each request carries about 100,000 tokens of contracts and policy, while each answer runs to about 10,000 tokens.
At list price, that workload costs $60,000 a month on Claude Opus 5.5 but only $9,100 on Mistral Large 4. So the real question in this Opus vs Mistral comparison is whether the pricier model earns the difference.
Which Claude Opus and Mistral Models Are We Comparing in 2026?
This comparison covers the model each vendor recommends for most enterprise work as of November 3, 2026. That matters because many aggregator pages still compare Claude Opus 4.x with Mistral Large 3, both a generation or more behind.
A price or benchmark quoted for those older versions tells you little about the models below. For the wider picture, our guide to enterprise LLM adoption covers strategy, while our ranking of the top LLMs places both in the full field.
Claude Opus 5.5: Anthropic’s Model for Serious Coding and Agents
Anthropic announced Claude Opus 5.5 on September 22, 2026. Its API id is claude-opus-5-5, and Anthropic’s model docs describe it as built for long-running agentic coding and knowledge work.
The price is $4 per million input tokens and $20 per million output tokens, which is 20% lower than Opus 5. Opus 5.5 matches or beats Fable 5.1 on most listed benchmarks, while Fable 5.1 lists at $10 and $50.
That makes Opus 5.5 the default pick inside the Claude family for most enterprise work. The Claude models overview also tells new users to start there.
Mistral Large 4: Mistral’s Open-Weight Flagship in Public Preview
Mistral Large 4 has been in public preview since October 6, 2026, under the API id mistral-large-4. It is a mixture-of-experts model with about 1T total parameters, while only 52B are active per token.
Mistral also publishes the weights on Hugging Face under its own open-weight license. List pricing on the Mistral API is $0.70 per million input tokens and $2.10 per million output tokens.
Where They Sit Next to GPT-6.1 Sol and Gemini 4 Argon
Neither model is the only option, so it helps to see the neighbors. The table lists prices per million tokens and context limits as of November 3, 2026.
Model Input / output per 1M tokens Context window
Claude Opus 5.5 $4 / $20 1M tokens Mistral Large 4 $0.70 / $2.10 1M tokens GPT-6.1 Sol $2 / $10 1,050,000 tokens Gemini 4 Argon $2 / $10 until November 30, 2026, then $4 / $20 from December 1, 2026 Up to 1M tokens
Opus 5.5 ties Gemini 4 Argon’s December 1 price and is otherwise the priciest of the four, while Large 4 is the cheapest. For the vendor-level story, read OpenAI vs Anthropic , and for the assistant view, see our ChatGPT vs Gemini vs Claude comparison.
Opus vs Mistral Side by Side: Specs, Pricing and License
The fastest way to see the gap is one spec sheet, because each row comes from the vendor’s own model page or model card.
Spec Claude Opus 5.5 Mistral Large 4
Status Announced September 22, 2026 Public preview since October 6, 2026 API id claude-opus-5-5 (Bedrock: anthropic.claude-opus-5-5) mistral-large-4 Price per 1M tokens (in / cached / out) $4 / $0.20 / $20, plus $5 per 1M for cache writes $0.70 / $0.07 / $2.10 Context window 1M tokens, with up to 128K output 1M tokens Parameters Not published About 1T total, 52B active Architecture Not published Mixture of experts: 128 routed experts, 4 active, 1 shared Inputs and outputs Text and image in, text out Text and image in, text out (1.6B vision encoder) License Proprietary, access through APIs only Modified MIT, but a Model-as-a-Service business needs a commercial license Where to run Claude Platform, AWS, Google Cloud, Microsoft Foundry Mistral API, or your own GPUs once downloaded Weights Not available mistralai/Mistral-Large-4-1T-A52B on Hugging Face
The table describes two different kinds of product, so compare them as products first. Opus 5.5 is a closed service that you rent per token from Anthropic or a cloud partner.
Large 4 is a model you can rent from Mistral, but you can also download and run it yourself. Its mixture-of-experts design activates only 52B parameters per token, so it needs less compute per token than a dense model of the same size. Our explainer on how mixture-of-experts LLMs are built covers the mechanics.
Opus 5.5 vs Mistral Large 4 Benchmarks: What the Vendors Report
Benchmarks are where the two vendors talk past each other, because each one publishes scores from its own runs. Across everything they publish, the two share only one test.
Benchmark Claude Opus 5.5 (Anthropic) Mistral Large 4 (Mistral)
Terminal-Bench 4.0 66.4% (xhigh effort) 41% (released model, October 29, 2026) DeepSWE v1.1 n/a 61.7% (preview launch, October 6, 2026) Cybench n/a 93% (preview launch, October 6, 2026) AutomationBench n/a 59.9% (preview launch, October 6, 2026)
Every figure above is vendor-reported, and n/a means the vendor published no score in the sources we checked. Read the table as two sets of vendor claims placed side by side, not as one shared test.
Dates matter on the Mistral side. Its DeepSWE, Cybench and AutomationBench scores are vendor-reported at the October 6, 2026 preview launch. The Terminal-Bench 4.0 score of 41% is for the released model, per Mistral’s October 29 post.
Terminal-Bench 4.0: The One Shared Benchmark
Terminal-Bench 4.0 tests how well a model completes tasks in a command-line environment. Anthropic reports 66.4% for Opus 5.5 at its xhigh effort setting, compared to 55.8% for Fable 5.1.
Mistral reports 41% for the released Large 4, per its October 29, 2026 post, up 13 points from the public preview. That leaves a gap of 25.4 points, since 66.4 minus 41 is 25.4.
The two figures come from each vendor’s own run with different test harnesses and effort levels, so this is not a head-to-head result. Treat the gap as directional rather than exact. Even so, a gap that wide fits each model’s stated focus.
Where Mistral Reports Strength: Security, Automation and Languages
Mistral’s Large 4 launch post leans on other tests instead. At the October 6, 2026 preview launch, it reported 61.7% on DeepSWE v1.1, 93% on Cybench and 59.9% on AutomationBench.
Cybench is a cybersecurity benchmark, so 93% backs Mistral’s pitch to security teams. The Large 4 model card also lists 160+ languages, including all official EU languages.
Mistral says that Opus 5.5 scores near zero on one Artificial Analysis Cyber Index test because it refuses the task. That test asks a model to reproduce and then patch a vulnerability. This is Mistral’s claim about a competitor rather than an independent finding, but it still points to a real tradeoff in refusal behavior.
Why Vendor Benchmarks Will Not Pick Your Model
A vendor chooses which benchmarks to publish, which settings to run and which rivals to show. None of that reflects your prompts, your documents or your tolerance for errors.
Treat these scores as a shortlist filter, then measure both models on your own tasks. Our LLM evaluation framework sets out how to build that test, while our guide to LLM hallucination explains which failure modes to score.
Kanerika Service
LLM Development Services
Kanerika selects, tests and deploys language models, closed or open-weight, with routing, monitoring and governance built in for enterprise workloads.
Explore LLM Development Opus vs Mistral Pricing: Cost per Task, Not Cost per Token
Token prices are easy to compare, but they are not what you pay for a finished job. What you really pay is cost per completed task, which depends on prompt size, output length and retries.
List Prices and Caching
Both vendors discount repeated prompt prefixes through caching. The table compares list prices per million tokens on each vendor’s own API, before any batch discount.
Price per 1M tokens Claude Opus 5.5 Mistral Large 4
Input $4 $0.70 Cached input (read) $0.20 $0.07 Cache write $5 Not listed separately Output $20 $2.10 Batch processing 50% off on the Batch API Batching supported
Anthropic also cut Opus 5.5 cache reads by 60% compared to Opus 5, which helps any workload with a stable system prompt. Mistral’s API pricing docs list Large 4 cached input at $0.07 per million tokens, so it costs about a third of the Opus rate.
Worked Example: One 100K-In, 10K-Out Request
Take a request with 100,000 input tokens, such as a long contract plus instructions, and a 10,000-token answer. Here is the math at list price, before any batch discount.
Opus 5.5 input costs 0.1M tokens x $4 = $0.40, while output costs 0.01M x $20 = $0.20, so the request totals $0.60.
Large 4 input costs 0.1M x $0.70 = $0.07, while output costs 0.01M x $2.10 = $0.021, so the request totals $0.091.
Opus 5.5 therefore costs about 6.6 times more per request.
At 100,000 requests a month, that is $60,000 on Opus 5.5 compared to $9,100 on Large 4.
When 80,000 of the input tokens come from cache, one request drops to $0.296 on Opus 5.5 and $0.0406 on Large 4.
The monthly bill then falls to $29,600 against $4,060, excluding the cache-write cost.
Caching halves the Opus bill, but Large 4 still stays about seven times cheaper per request. The picture only flips when Opus 5.5 finishes tasks in fewer attempts or Large 4 needs heavy human review.
Costs the Price List Leaves Out
The list price is the floor of your bill rather than the bill itself. Five costs move the real number.
Output length: output tokens cost 5x input on Opus 5.5 and 3x on Large 4, so verbose answers add up fast.
Retries: when you run a failed answer again, you pay for that task twice.
Cache hit rate: the 80% example above assumes a stable prompt prefix, so a low hit rate erases most of the saving.
Self-hosting: open weights remove token fees but add a multi-GPU server, power and on-call engineers.
Preview risk: Large 4 is still in public preview, so budget time for regression tests when it changes.
Some of those tasks do not need a frontier model at all. For classification and extraction, small language models for high-volume tasks such as Mistral Small 4 at $0.15 and $0.60 can cut the bill again. An LLM gateway then sends each request to the cheapest model that passes your tests.
Deployment: Managed API vs Self-Hosted Open Weights
Where a model can run often settles the choice before quality enters the discussion. Claude Opus 5.5 is a managed service only, while Large 4 can be rented or run on your own hardware.
Opus 5.5 Runs Where Your Cloud Already Is
You can buy Opus 5.5 on the Claude Platform, AWS, Google Cloud and Microsoft Foundry. On Amazon Bedrock, for example, its id is anthropic.claude-opus-5-5.
That spread matters for procurement, because a team can buy Opus 5.5 under an existing cloud contract. Traffic then stays inside an account the team already governs. Anthropic’s docs also note that Bedrock offers regional endpoints with guaranteed data routing.
Mistral Large 4 on the Mistral API or Your Own GPUs
Large 4 runs on the Mistral API, or you can download the official weights from Hugging Face. The repository went up on September 30, 2026, and the download is ungated.
Mistral supports vLLM and SGLang for serving, while an NVFP4 checkpoint targets NVIDIA Blackwell GPUs. Our comparison of SGLang vs vLLM helps you pick the serving engine.
Plan for a multi-GPU server rather than a single card. Once it runs, the deployment becomes a private LLM that never sends prompts outside your network.
The License Fine Print
Open weight is not the same as open source in the OSI sense. Large 4 ships under the Mistral Ecosystem Open-Weight License, a modified MIT license that Hugging Face tags as other.
The main catch concerns Model-as-a-Service, that is, offering Large 4 to others as a hosted model. That use needs a separate commercial license from Mistral.
Internal use and use through Mistral’s official products are exempt from that clause, but have legal read the full license text before production. For contrast, DeepSeek’s MIT-licensed weights carry no such clause, while our guide to open-source LLMs compares license terms across the field.
Coding, AI Agents and Tool Use
Both models are built to call tools, so the real question is how each handles long, multi-step work. Tool calling is also the base that agent frameworks build on. Our explainer on MCP vs A2A sets out what each protocol expects, so check each vendor’s current docs before you assume support for one.
A feature list matters only when it removes work you would otherwise build. Picture an invoice-extraction agent. It calls a function to fetch the supplier record, returns structured output in one JSON shape and reads scanned pages as images. Mistral documents all three for Large 4, with a 1.6B vision encoder, alongside document QnA, batching, built-in tools and an agents and conversations API.
On the Claude side, Anthropic lists tool use and image input for all current models, so Opus 5.5 can call the same supplier lookup and read the same scans. Its adaptive thinking is always on at a default effort of medium, which you can raise to xhigh for the hardest invoices. Large 4 offers reasoning effort at low, medium or high, so both models let you trade cost for depth on each call.
Long Coding and Agent Runs
Anthropic positions Opus 5.5 for long-running agentic coding, and the Terminal-Bench gap supports that focus. A coding agent may chain many tool calls per task, so one weak step can sink the whole run.
If your workload looks like that, start the bake-off with Opus 5.5, then test whether Large 4 is good enough on the easier tickets. Our guide to AI coding agents covers the agent side in depth.
Keep the model and the product apart, too. A model is what you call through an API, while assistant products such as Copilot and ChatGPT wrap models in their own interface and policies.
Long Context and Multilingual Work
Both models accept 1M tokens, which is enough for a large contract set or a mid-sized code base in one prompt. A large window does not guarantee the model finds the detail you need, though, so test recall on your own long documents.
Large 4 also adds breadth on language, with 160+ languages including every official EU language. That matters for a support desk answering customers in Polish and Greek, because one prompt set and one test suite can then cover both markets instead of one per language.
Security, Data Residency and Governance
For regulated teams, the first question is where prompts and outputs travel. The answer differs sharply between a managed model and open weights, so settle it before any quality test.
Where Your Prompts Go
Opus 5.5 traffic goes to Anthropic or to the cloud you buy it through, under that provider’s terms. Teams already approved on AWS, Google Cloud or Microsoft Foundry can therefore reuse an existing security review.
Mistral trained Large 4 on 3,800 NVIDIA Grace Blackwell GPUs in its European datacenters. At launch, Mistral said it would serve the model from several regions, including a European deployment run end to end under European law.
Self-hosting the weights keeps every prompt on your own servers, which is the strongest residency position either model allows. That control is also the core of a sovereign AI strategy.
Refusals, Red-Teaming and Security Research
Mistral argues that Large 4 helps security researchers, pointing to its 93% Cybench result from the October 6, 2026 preview launch. It also says Opus 5.5 refuses some offensive security tasks, which is Mistral’s framing of a competitor.
In a bank or a hospital, a refusal on an exploit request is a control rather than a defect. A security research team may see the same refusal as a blocker, so decide which behavior your use case needs. Our LLM security guide lists the threats to test for with either model.
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Governance Controls You Own Either Way
No model choice moves your governance duties to the vendor. The NIST AI Risk Management Framework , released January 26, 2023, is a voluntary framework that you can map controls to.
The EU AI Act applied its general-purpose AI rules from August 2025, then its transparency rules from August 2026. High-risk obligations follow from December 2, 2027. Map each workload against those dates early, because a support bot and a credit-scoring model face very different duties.
Five controls apply to Opus 5.5 and Large 4 alike, even if you route between them.
A model inventory that records version, vendor, region and data tier, so every workload has an owner.
Data classification that blocks restricted data unless the model runs in an approved region.
Logged prompts and outputs, kept until your retention policy says otherwise.
Human review for high-impact decisions, such as credit or medical calls, at sampling rates set per workload.
A re-test gate whenever a vendor ships a new version or a preview model changes.
Our AI governance framework turns these controls into a working program with owners and review dates.
Checklist
Generative AI Checklist
Work through data, security, model and governance questions before a generative AI rollout, including where each workload should run and who signs off.
Get the Checklist → Which Model for Which Enterprise Workload?
The right answer changes by workload, so score each one on its own. The matrix below rates Opus vs Mistral fit from 1 to 5, based on the prices, scores and deployment options covered above.
Workload Opus 5.5 Large 4 Pick
Coding agents and long tool chains 5 3 Opus 5.5 Long-document analysis and review 5 4 Opus 5.5 Regulated reasoning in finance, legal or health 5 3 Opus 5.5 High-volume customer support 3 5 Large 4 Multilingual EU content 4 5 Large 4 Security research 3 4 Large 4, after a refusal test Sovereign or on-premises deployment 1 5 Large 4 Document QnA at scale 3 5 Large 4
The scores are our editorial judgment from the evidence above rather than measured results. Opus 5.5 wins where an error costs more than the tokens, while Large 4 wins where volume, residency or language breadth decides the case.
Choose Opus 5.5 When
Your agents run long coding or tool chains, so one failed step wastes the whole run.
You want the strongest vendor-reported agentic score, since Anthropic reports 66.4% on Terminal-Bench 4.0.
Your security team has approved AWS, Google Cloud or Microsoft Foundry, so you can buy through that contract.
Errors are expensive, for example in legal review, financial analysis or production code.
Choose Mistral Large 4 When
Request volume is high, so the 6.6x price gap per request dominates the budget.
Prompts may never leave your servers, because only open weights allow that.
You serve customers in many EU languages, such as German, Polish or Greek.
You can accept preview status once versions are pinned behind regression tests.
Route Both Through One Gateway
A typical enterprise portfolio holds both kinds of work, which means a single-model policy either overpays on simple tasks or underperforms on hard ones.
Routing sends each request to the cheaper model unless a rule or a failed check escalates it. In the worked example, moving half of the 100,000 monthly requests to Large 4 cuts the uncached bill from $60,000 to $34,550.
Both models sit inside the wider family of generative AI models that one gateway can manage side by side, with shared logging and policy.
How to Run an Opus vs Mistral Bake-Off Before Production
A bake-off on your own data settles what no leaderboard can. Run it in six steps, and keep both models on identical inputs.
Build a golden set of a few hundred real tasks with approved answers, drawn from your own tickets, documents or code.
Define the metrics before testing, such as accuracy, refusal rate, latency and cost per completed task.
Run both models with the same prompts, tools and context, then record every retry.
Shadow live traffic, so the challenger answers copies of real requests while users never see its output.
Have domain reviewers grade a blind sample, so nobody knows which model wrote which answer.
Make the go or no-go call per workload, then write the routing rule into your gateway.
The output is a routing table rather than a single winner. Expect Opus 5.5 to take the hard tail while Large 4 takes the bulk, then let your numbers confirm or overturn that.
How Kanerika Helps Enterprises Choose and Deploy the Right LLM
Kanerika’s LLM development team treats model choice as an engineering decision tested on client data. The same approach also runs through our generative AI and agentic AI practices, in five stages.
Workload inventory, with each use case tiered by data sensitivity before any model is chosen.
A shortlist and bake-off on the client’s own golden set, scored on cost per completed task.
A deployment pattern, either a managed API in the client’s cloud or self-hosted open weights, plus an LLM gateway for routing.
Governance controls mapped to NIST AI RMF and EU AI Act obligations, delivered with our AI governance team.
Production monitoring of quality and spend for every routed workload.
Each stage ends with a written decision that the client owns. A model choice made this way stays reversible, because the golden set, the gateway rules and the cost data remain with the client team.
Two Kanerika products often sit around that work. FLIP builds and runs the data pipelines that feed LLM workloads, while Karl is a data insights agent that answers business questions over enterprise data.
Proof From a High-Volume Support Desk
High-volume support is where cost per task decides the model. For a B2B SaaS company serving SMB clients in 40+ countries, Kanerika built a knowledge base from historical tickets and an LLM-based resolution system.
The result was 80% of tickets answered automatically, 70% lower staffing costs and a 50% cut in resolution time. Our guide to AI agents for customer support covers that pattern in more depth.
So which LLM fits enterprise GenAI? Pick Opus 5.5 for the hard, high-stakes tail and Mistral Large 4 for volume and control. Then settle each Opus vs Mistral call workload by workload, on your own data.
Case Study
80% Ticket Auto-Resolution with LLM-Driven AI
A B2B SaaS company serving SMB clients in 40+ countries used a knowledge base from historical tickets and an LLM-based resolution system to auto-answer 80% of tickets, cut staffing costs by 70% and halve resolution time.
Read the Case Study → Frequently Asked Questions
What is the difference between Claude Opus 5.5 and Mistral Large 4?
Claude Opus 5.5 is a closed Anthropic model sold through the Claude API and major clouds. Mistral Large 4 is an open-weight model in public preview, with weights on Hugging Face. Both read 1M tokens of context. Opus 5.5 costs $4 and $20 per million input and output tokens, while Large 4 costs $0.70 and $2.10.
Which is better, Claude Opus or Mistral Large?
Neither wins every workload. Claude Opus 5.5 is the stronger pick for coding agents, long tool chains and high-stakes reasoning, and Anthropic reports 66.4% on Terminal-Bench 4.0. Mistral Large 4 suits high-volume, multilingual and self-hosted work at a much lower price. Test both on your own tasks and route each workload to the winner.
What is Mistral AI best at?
Mistral AI is strongest where price, open weights and European control matter. Large 4 lists at $0.70 and $2.10 per million tokens and supports 160+ languages, including every official EU language. Mistral also reported 93% on Cybench at its October 6 preview. Smaller models such as Mistral Small 4 at $0.15 and $0.60 cover high-volume tasks at even lower cost.
Is Claude Opus 5.5 the best AI model?
No single model is best for every task. Anthropic recommends Opus 5.5 for most workloads and says it matches or beats Fable 5.1 on most listed benchmarks, at $4 and $20 against $10 and $50. GPT-6.1 Sol and Gemini 4 Argon compete at lower token prices. The best model is the one that wins your own tests.
Who are Mistral AI's main competitors?
Mistral competes with Anthropic, OpenAI and Google on frontier models, and with other open-weight publishers such as DeepSeek. In the closed tier, its rivals include Claude Opus 5.5, GPT-6.1 Sol and Gemini 4 Argon. Among open-weight models, the comparison turns on license terms, model size and serving cost rather than benchmark headlines alone.
Is Mistral AI better than ChatGPT?
They are different kinds of products. ChatGPT is OpenAI’s assistant app built on its GPT models, while Mistral Large 4 is a model you call through an API or host yourself. Large 4 is cheaper per token and comes with open weights. For a fair model test, compare Large 4 with GPT-6.1 Sol, which costs $2 and $10.
Which is better for coding and AI agents, Opus 5.5 or Mistral Large 4?
Claude Opus 5.5 is the stronger choice on vendor-reported results. Anthropic reports 66.4% on Terminal-Bench 4.0 at xhigh effort, while Mistral reports 41% for the released Large 4, per its October 29 post. The setups differ, so the gap is directional. Use Opus 5.5 for complex agents and test Large 4 on simpler, high-volume coding tasks.
Does Mistral Large 4 support function calling and tools?
Yes. Mistral lists function calling, structured outputs, document question answering, batching, an agents and conversations API and built-in tools for Large 4. It also exposes reasoning effort at low, medium or high. Before you depend on a specific agent protocol, confirm support in the current Mistral documentation, because these features change between releases.
Is Mistral Large 4 generally available?
Not yet. Mistral’s documentation labels Large 4 as Public Preview, a status it has held since October 6, 2026. Preview models can change before general availability. Pin the model version, keep a regression test suite, and plan a fallback route through a gateway before you put Large 4 into a customer-facing workflow.
Who is OpenAI's biggest competitor?
Anthropic and Google are OpenAI’s closest rivals at the frontier. Anthropic sells Claude Opus 5.5 and Fable 5.1, and Google sells Gemini 4 Argon, priced at $2 and $10 per million tokens through November 30, 2026. Mistral and DeepSeek compete from the open-weight side, often at much lower prices per token.
Can enterprises use Opus 5.5 and Mistral Large 4 together?
Yes, and it is often the cheapest safe setup. An LLM gateway sends routine requests to Large 4 and escalates hard or high-risk ones to Opus 5.5. In our worked example, moving half of 100,000 monthly requests to Large 4 cuts the bill from $60,000 to $34,550. Keep logs and routing rules under one policy.
Which is cheaper, Claude Opus 5.5 or Mistral Large 4?
Mistral Large 4 is far cheaper at list price. It costs $0.70 per million input tokens and $2.10 per million output tokens, against $4 and $20 for Opus 5.5. A request with 100,000 input and 10,000 output tokens costs $0.091 on Large 4 and $0.60 on Opus 5.5, about 6.6 times more.
How expensive is Claude Opus 5.5?
Claude Opus 5.5 lists at $4 per million input tokens and $20 per million output tokens. Cache reads cost $0.20 per million, and cache writes cost $5 per million. The Batch API takes 50% off list price. These token prices are 20% lower than Opus 5, and the Fable 5.1 tier costs $10 and $50.
Which has the larger context window, Opus 5.5 or Mistral Large 4?
Neither has an edge on paper. Claude Opus 5.5 and Mistral Large 4 both accept up to 1M tokens of context, and Opus 5.5 can return up to 128K output tokens. A large window does not guarantee good recall, so test each model on your own long documents before you rely on it in production.
Can Mistral Large 4 be self-hosted?
Yes. Mistral publishes the official weights in the mistralai/Mistral-Large-4-1T-A52B repository on Hugging Face, and the download is ungated. Mistral supports vLLM and SGLang for serving, plus an NVFP4 checkpoint for NVIDIA Blackwell GPUs. The model has about 1T total parameters, so plan for a multi-GPU server rather than a single card.
Is Mistral Large 4 open source?
Mistral Large 4 is open weight, not open source in the OSI sense. It ships under the Mistral Ecosystem Open-Weight License, a modified MIT license. You can download and run the weights yourself. Running it inside a Model-as-a-Service business needs a separate commercial license from Mistral, while internal use is exempt, so check the terms first.
What are the main enterprise use cases for Opus vs Mistral?
Opus 5.5 fits coding agents, long multi-step tool use, contract and policy review, and regulated analysis where errors are costly. Mistral Large 4 fits high-volume support, document question answering, multilingual European content and on-premises deployment. Many teams use both, sending hard tasks to Opus 5.5 and bulk traffic to Large 4 through one gateway.
Which industries benefit most from Opus vs Mistral?
Software, legal and financial services teams gain most from Opus 5.5 on complex reasoning and code. Public sector, healthcare and European firms with strict residency rules may favor Large 4, because its open weights can run on their own servers. Retail and telecom support desks gain from its lower price at high request volumes.
How do Opus and Mistral handle data security and residency?
Opus 5.5 runs on the Claude Platform, AWS, Google Cloud and Microsoft Foundry, so traffic stays with a provider you already govern. At launch, Mistral said it would serve Large 4 from several regions, including a European deployment under European law. Self-hosting the Large 4 weights keeps every prompt on your own servers, the strongest residency option.
