Mistral Large vs Qwen3.5-9B
Mistral Large (2024) and Qwen3.5-9B (2026) are compact production models from MistralAI and Alibaba. Mistral Large ships a 32k-token context window, while Qwen3.5-9B ships a 262K-token context window. On MMLU PRO, Qwen3.5-9B leads by 31 pts. On pricing, Qwen3.5-9B costs $0.1/1M input tokens versus $0.32/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen3.5-9B is ~220% cheaper at $0.1/1M; pay for Mistral Large only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Mistral Large | Qwen3.5-9B |
|---|---|---|
| Decision fit | Agents, Vision, and Classification | RAG, Agents, and Long context |
| Context window | 32k | 262K |
| Cheapest output | $0.96/1M tokens | $0.15/1M tokens |
| Provider routes | 8 tracked | 3 tracked |
| Shared benchmarks | 1 rows | MMLU PRO leader |
Decision tradeoffs
- Mistral Large has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Mistral Large for Agents, Vision, and Classification.
- Qwen3.5-9B leads the largest shared benchmark signal on MMLU PRO by 31 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.
- Qwen3.5-9B uniquely exposes Multimodal 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
$496
Cheapest tracked route: GCP Vertex AI
Qwen3.5-9B
$118
Cheapest tracked route: Together AI
Estimated monthly gap: $379. 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 $0.81/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Qwen3.5-9B adds Multimodal in local capability data.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Mistral Large is $0.81/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-08 | 2026-03-02 |
| Context window | 32k | 262K |
| Parameters | — | 9B |
| Architecture | - | decoder only |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | 2024-03 | - |
Pricing and availability
| Pricing attribute | Mistral Large | Qwen3.5-9B |
|---|---|---|
| Input price | $0.32/1M tokens | $0.1/1M tokens |
| Output price | $0.96/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Large | Qwen3.5-9B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Mistral Large | Qwen3.5-9B |
|---|---|---|
| MMLU PRO | 51.5 | 82.5 |
Deep dive
On shared benchmark coverage, MMLU PRO has Mistral Large at 51.5 and Qwen3.5-9B at 82.5, with Qwen3.5-9B ahead by 31 points. The largest visible gap is 31 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 multimodal input: Qwen3.5-9B. Both models share vision, function calling, tool use, 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 lists $0.32/1M input and $0.96/1M 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 $0.4 per million blended tokens. Availability is 8 providers versus 3, so concentration risk also matters.
Choose Mistral Large 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 or Qwen3.5-9B?
Qwen3.5-9B supports 262K tokens, while Mistral Large supports 32k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, Mistral Large or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. Mistral Large costs $0.32/1M input and $0.96/1M 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 or Qwen3.5-9B open source?
Mistral Large is listed under Proprietary. 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 or Qwen3.5-9B?
Both Mistral Large 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 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 and Qwen3.5-9B?
Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. 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.