Mistral Large 2 (2407) vs Qwen3.5-9B
Mistral Large 2 (2407) (2024) and Qwen3.5-9B (2026) are compact production models from MistralAI and Alibaba. Mistral Large 2 (2407) ships a 128K-token context window, while Qwen3.5-9B ships a 262K-token context window. On pricing, Qwen3.5-9B costs $0.1/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3.5-9B is ~400% cheaper at $0.1/1M; pay for Mistral Large 2 (2407) only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Mistral Large 2 (2407) | Qwen3.5-9B |
|---|---|---|
| Decision fit | RAG, Long context, and Vision | RAG, Agents, and Long context |
| Context window | 128K | 262K |
| Cheapest output | $1.5/1M tokens | $0.15/1M tokens |
| Provider routes | 3 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Mistral Large 2 (2407) for RAG, Long context, and Vision.
- Qwen3.5-9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-9B has the lower cheapest tracked output price at $0.15/1M tokens.
- Qwen3.5-9B uniquely exposes Multimodal, Function calling, and Tool use in local model data.
- Local decision data tags Qwen3.5-9B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Mistral Large 2 (2407)
$775
Cheapest tracked route: Chutes AI
Qwen3.5-9B
$118
Cheapest tracked route: Together AI
Estimated monthly gap: $658. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for Mistral Large 2 (2407) and Qwen3.5-9B; plan for SDK, billing, or endpoint changes.
- Qwen3.5-9B is $1.35/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Qwen3.5-9B adds Multimodal, Function calling, and Tool use in local capability data.
- No overlapping tracked provider route is sourced for Qwen3.5-9B and Mistral Large 2 (2407); plan for SDK, billing, or endpoint changes.
- Mistral Large 2 (2407) is $1.35/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Multimodal, Function calling, and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2026-03-02 |
| Context window | 128K | 262K |
| Parameters | 123B | 9B |
| Architecture | decoder only | decoder only |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | 2024-03 | - |
Pricing and availability
| Pricing attribute | Mistral Large 2 (2407) | Qwen3.5-9B |
|---|---|---|
| Input price | $0.5/1M tokens | $0.1/1M tokens |
| Output price | $1.5/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Large 2 (2407) | Qwen3.5-9B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Qwen3.5-9B, function calling: Qwen3.5-9B, and tool use: Qwen3.5-9B. Both models share vision 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, Mistral Large 2 (2407) lists $0.5/1M input and $1.5/1M output tokens, while Qwen3.5-9B lists $0.1/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-9B lower by about $0.68 per million blended tokens. Availability is 3 providers versus 3, so concentration risk also matters.
Choose Mistral Large 2 (2407) when vision-heavy evaluation are central to the workload. Choose Qwen3.5-9B when long-context analysis, larger context windows, 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, Mistral Large 2 (2407) or Qwen3.5-9B?
Qwen3.5-9B supports 262K 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, Mistral Large 2 (2407) or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. Mistral Large 2 (2407) costs $0.5/1M input and $1.5/1M output tokens. Qwen3.5-9B costs $0.1/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mistral Large 2 (2407) or Qwen3.5-9B open source?
Mistral Large 2 (2407) is listed under Apache 2.0. Qwen3.5-9B is listed under Apache 2.0. 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, Mistral Large 2 (2407) or Qwen3.5-9B?
Both Mistral Large 2 (2407) and Qwen3.5-9B expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Mistral Large 2 (2407) or Qwen3.5-9B?
Qwen3.5-9B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Mistral Large 2 (2407) and Qwen3.5-9B?
Mistral Large 2 (2407) is available on Microsoft Foundry, Chutes AI, and SiliconFlow. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.