Mistral Large 2 vs Qwen3.5-9B
Mistral Large 2 (2025) and Qwen3.5-9B (2026) are compact production models from MistralAI and Alibaba. Mistral Large 2 ships a 128K-token context window, while Qwen3.5-9B ships a 262K-token context window. On MMLU PRO, Qwen3.5-9B leads by 12.8 pts. On pricing, Qwen3.5-9B costs $0.1/1M input tokens versus $0.48/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen3.5-9B is ~380% cheaper at $0.1/1M; pay for Mistral Large 2 only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Mistral Large 2 | Qwen3.5-9B |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | RAG, Agents, and Long context |
| Context window | 128K | 262K |
| Cheapest output | $2.4/1M tokens | $0.15/1M tokens |
| Provider routes | 4 tracked | 3 tracked |
| Shared benchmarks | 1 rows | MMLU PRO leader |
Decision tradeoffs
- Mistral Large 2 has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Mistral Large 2 for Coding, RAG, and Agents.
- Qwen3.5-9B leads the largest shared benchmark signal on MMLU PRO by 12.8 points.
- 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.
- 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
$984
Cheapest tracked route: AWS Bedrock
Qwen3.5-9B
$118
Cheapest tracked route: Together AI
Estimated monthly gap: $867. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Qwen3.5-9B is $2.25/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Mistral Large 2 is $2.25/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-11-25 | 2026-03-02 |
| Context window | 128K | 262K |
| Parameters | 123B | 9B |
| Architecture | decoder only | decoder only |
| License | True | Apache 2.0 |
| Knowledge cutoff | 2025-07 | - |
Pricing and availability
| Pricing attribute | Mistral Large 2 | Qwen3.5-9B |
|---|---|---|
| Input price | $0.48/1M tokens | $0.1/1M tokens |
| Output price | $2.4/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Large 2 | Qwen3.5-9B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Mistral Large 2 | Qwen3.5-9B |
|---|---|---|
| MMLU PRO | 69.7 | 82.5 |
Deep dive
On shared benchmark coverage, MMLU PRO has Mistral Large 2 at 69.7 and Qwen3.5-9B at 82.5, with Qwen3.5-9B ahead by 12.8 points. The largest visible gap is 12.8 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 is close: both models cover vision, multimodal input, function calling, tool use, and structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Mistral Large 2 lists $0.48/1M input and $2.4/1M 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 $0.94 per million blended tokens. Availability is 4 providers versus 3, so concentration risk also matters.
Choose Mistral Large 2 when vision-heavy evaluation and broader provider choice 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.
FAQ
Which has a larger context window, Mistral Large 2 or Qwen3.5-9B?
Qwen3.5-9B supports 262K tokens, while Mistral Large 2 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 or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. Mistral Large 2 costs $0.48/1M input and $2.4/1M 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 or Qwen3.5-9B open source?
Mistral Large 2 is listed under True. 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 or Qwen3.5-9B?
Both Mistral Large 2 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 or Qwen3.5-9B?
Both Mistral Large 2 and Qwen3.5-9B expose multimodal input. 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.
Where can I run Mistral Large 2 and Qwen3.5-9B?
Mistral Large 2 is available on OpenRouter, IBM watsonx, AWS Bedrock, and Mistral AI Studio. 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.