DeepSeek V3.1 vs Mistral Large 2 (2407)
DeepSeek V3.1 (2025) and Mistral Large 2 (2407) (2024) are compact production models from DeepSeek and MistralAI. Both ship a 128K-token context window. On MMLU PRO, DeepSeek V3.1 leads by 17.4 pts. On pricing, DeepSeek V3.1 costs $0.27/1M input tokens versus $0.50/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
DeepSeek V3.1 is ~85% cheaper at $0.27/1M; pay for Mistral Large 2 (2407) only for provider fit.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.1 | Mistral Large 2 (2407) |
|---|---|---|
| Best for | provider-routed production | tool-calling agents and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 128K | 128K |
| Cheapest output | $1/1M tokens | $1.50/1M tokens |
| Provider routes | 10 tracked | 7 tracked |
| Shared benchmarks | MMLU PRO leader | 1 shared |
Decision tradeoffs
- DeepSeek V3.1 holds a shared-benchmark lead on MMLU PRO, ahead by 17.4 points.
- DeepSeek V3.1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- DeepSeek V3.1 has the lower cheapest tracked output price at $1/1M tokens.
- DeepSeek V3.1 has broader tracked provider coverage for fallback and route flexibility.
- DeepSeek V3.1 uniquely exposes Code execution in local model data.
- Mistral Large 2 (2407) uniquely exposes JSON / Tool use in local model data.
- Local decision data tags Mistral Large 2 (2407) 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.
DeepSeek V3.1
$466
Cheapest tracked route/tier: Novita AI
Mistral Large 2 (2407)
$775
Cheapest tracked route/tier: Chutes AI
Estimated monthly gap: $309. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Microsoft Foundry, OpenRouter, and AWS Bedrock; start route-level A/B tests there.
- Mistral Large 2 (2407) is $0.50/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Code execution before moving production traffic.
- Mistral Large 2 (2407) adds JSON / Tool use in local capability data.
- Provider overlap exists on Microsoft Foundry, OpenRouter, and AWS Bedrock; start route-level A/B tests there.
- DeepSeek V3.1 is $0.50/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for JSON / Tool use before moving production traffic.
- DeepSeek V3.1 adds Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-08-21 | 2024-07-24 |
| Context window | 128K | 128K |
| Parameters | 671B total, 37B active (MoE) | 123B |
| Architecture | Mixture of Experts | Decoder Only |
| License | MITOSI-approved | Mistral License |
| Openness | Open source | Open weights |
| Weights | Unknown | Available |
| Code | Unknown | Unknown |
| Commercial use | Permitted | Not permitted |
| Knowledge cutoff | - | 2024-03 |
Pricing and availability
| Pricing attribute | DeepSeek V3.1 | Mistral Large 2 (2407) |
|---|---|---|
| Input price | $0.27/1M tokens | $0.50/1M tokens |
| Output price | $1/1M tokens | $1.50/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3.1 | Mistral Large 2 (2407) |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| JSON / Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V3.1 | Mistral Large 2 (2407) |
|---|---|---|
| MMLU PRO | 83.3 | 65.9 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V3.1 at 83.3 and Mistral Large 2 (2407) at 65.9, with DeepSeek V3.1 ahead by 17.4 points. The largest visible gap is 17.4 points on MMLU PRO, 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 JSON / Tool use: Mistral Large 2 (2407) and code execution: DeepSeek V3.1. Both models share 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, DeepSeek V3.1 lists $0.27/1M input and $1/1M output tokens on the cheapest tracked provider, while Mistral Large 2 (2407) lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek V3.1 lower by about $0.31 per million blended tokens. Availability is 10 providers versus 7, so concentration risk also matters.
Choose DeepSeek V3.1 when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose Mistral Large 2 (2407) when provider fit 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, DeepSeek V3.1 or Mistral Large 2 (2407)?
DeepSeek V3.1 supports 128K tokens, while Mistral Large 2 (2407) 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, DeepSeek V3.1 or Mistral Large 2 (2407)?
DeepSeek V3.1 is cheaper on tracked token pricing. DeepSeek V3.1 costs $0.27/1M input and $1/1M output tokens. Mistral Large 2 (2407) costs $0.50/1M input and $1.50/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.1 or Mistral Large 2 (2407) open source?
DeepSeek V3.1 is listed under MIT. Mistral Large 2 (2407) 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 JSON / Tool use, DeepSeek V3.1 or Mistral Large 2 (2407)?
Mistral Large 2 (2407) has the clearer documented JSON / Tool use signal in this comparison. If JSON / Tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, DeepSeek V3.1 or Mistral Large 2 (2407)?
Both DeepSeek V3.1 and Mistral Large 2 (2407) expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run DeepSeek V3.1 and Mistral Large 2 (2407)?
DeepSeek V3.1 is available on Microsoft Foundry, Fireworks AI, OpenRouter, NVIDIA NIM, and Together AI. Mistral Large 2 (2407) is available on Microsoft Foundry, IBM watsonx, OpenRouter, AWS Bedrock, and Chutes AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-10-17. Data sourced from public model cards and provider documentation.