Claude Sonnet 4.6 vs Mistral Large 3 675B Instruct
Claude Sonnet 4.6 (2026) and Mistral Large 3 675B Instruct (2025) are frontier reasoning models from Anthropic and MistralAI. Claude Sonnet 4.6 ships a 1m-token context window, while Mistral Large 3 675B Instruct ships a 128k-token context window. On MMLU PRO, Claude Sonnet 4.6 leads by 1.8 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Mistral Large 3 675B Instruct is ~500% cheaper at $0.50/1M; pay for Claude Sonnet 4.6 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | Claude Sonnet 4.6 | Mistral Large 3 675B Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | multimodal apps and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1m | 128k |
| Cheapest output | $15/1M tokens | $1.50/1M tokens |
| Provider routes | 6 tracked | 5 tracked |
| Shared benchmarks | MMLU PRO leader | 5 rows |
Decision tradeoffs
- Claude Sonnet 4.6 holds a shared-benchmark lead on MMLU PRO, ahead by 1.8 points.
- Claude Sonnet 4.6 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Claude Sonnet 4.6 has broader tracked provider coverage for fallback and procurement flexibility.
- Claude Sonnet 4.6 uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags Claude Sonnet 4.6 for Coding, RAG, and Agents.
- Mistral Large 3 675B Instruct holds a shared-benchmark lead on LiveCodeBench, ahead by 2.8 points.
- Mistral Large 3 675B Instruct has the lower cheapest tracked output price at $1.50/1M tokens.
- Local decision data tags Mistral Large 3 675B Instruct for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Claude Sonnet 4.6
$6,150
Cheapest tracked route/tier: OpenRouter
Mistral Large 3 675B Instruct
$775
Cheapest tracked route/tier: AWS Bedrock
Estimated monthly gap: $5,375. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on AWS Bedrock, Microsoft Foundry, and Vercel AI Gateway; start route-level A/B tests there.
- Mistral Large 3 675B Instruct is $13.50/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Provider overlap exists on AWS Bedrock, Microsoft Foundry, and Vercel AI Gateway; start route-level A/B tests there.
- Claude Sonnet 4.6 is $13.50/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Claude Sonnet 4.6 adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-17 | 2025-12-01 |
| Context window | 1m | 128k |
| Parameters | — | 675B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Mistral License |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Non-commercial only |
| Knowledge cutoff | 2025-12 | 2024-11 |
Pricing and availability
| Pricing attribute | Claude Sonnet 4.6 | Mistral Large 3 675B Instruct |
|---|---|---|
| Input price | $3/1M tokens | $0.50/1M tokens |
| Output price | $15/1M tokens | $1.50/1M tokens |
| Providers |
Capabilities
| Capability | Claude Sonnet 4.6 | Mistral Large 3 675B Instruct |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | Yes | No |
| Parallel agents | Yes | No |
Benchmarks
| Benchmark | Claude Sonnet 4.6 | Mistral Large 3 675B Instruct |
|---|---|---|
| MMLU PRO | 87.3 | 85.5 |
| Google-Proof Q&A | 89.9 | 43.9 |
| LiveCodeBench | 80.0 | 82.8 |
| HumanEval | 98.0 | 92.0 |
| τ-bench | 87.5 | 70.2 |
Deep dive
On shared benchmark coverage, MMLU PRO has Claude Sonnet 4.6 at 87.3 and Mistral Large 3 675B Instruct at 85.5, with Claude Sonnet 4.6 ahead by 1.8 points; Google-Proof Q&A has Claude Sonnet 4.6 at 89.9 and Mistral Large 3 675B Instruct at 43.9, with Claude Sonnet 4.6 ahead by 46.0 points; LiveCodeBench has Claude Sonnet 4.6 at 80 and Mistral Large 3 675B Instruct at 82.8, with Mistral Large 3 675B Instruct ahead by 2.8 points. The largest visible gap is 46.0 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: Claude Sonnet 4.6, function calling: Claude Sonnet 4.6, tool use: Claude Sonnet 4.6, and code execution: Claude Sonnet 4.6. Both models share vision, multimodal input, and structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, Claude Sonnet 4.6 lists $3/1M input and $15/1M output tokens on the cheapest tracked provider, while Mistral Large 3 675B Instruct lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral Large 3 675B Instruct lower by about $5.80 per million blended tokens. Availability is 6 providers versus 5, so concentration risk also matters.
Choose Claude Sonnet 4.6 when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Mistral Large 3 675B Instruct when vision-heavy evaluation and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Claude Sonnet 4.6 or Mistral Large 3 675B Instruct?
Claude Sonnet 4.6 supports 1m tokens, while Mistral Large 3 675B Instruct supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Claude Sonnet 4.6 or Mistral Large 3 675B Instruct?
Mistral Large 3 675B Instruct is cheaper on tracked token pricing. Claude Sonnet 4.6 costs $3/1M input and $15/1M output tokens. Mistral Large 3 675B Instruct costs $0.50/1M input and $1.50/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Claude Sonnet 4.6 or Mistral Large 3 675B Instruct open source?
Claude Sonnet 4.6 is listed under Proprietary. Mistral Large 3 675B Instruct is listed under Mistral License. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, Claude Sonnet 4.6 or Mistral Large 3 675B Instruct?
Both Claude Sonnet 4.6 and Mistral Large 3 675B Instruct expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for multimodal input, Claude Sonnet 4.6 or Mistral Large 3 675B Instruct?
Both Claude Sonnet 4.6 and Mistral Large 3 675B Instruct expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Claude Sonnet 4.6 and Mistral Large 3 675B Instruct?
Claude Sonnet 4.6 is available on OpenRouter, Anthropic, AWS Bedrock, GCP Vertex AI, and Microsoft Foundry. Mistral Large 3 675B Instruct is available on AWS Bedrock, NVIDIA NIM, Mistral AI Studio, Microsoft Foundry, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.