Mistral Medium 3.5 vs Qwen3-235B-A22B
Mistral Medium 3.5 (2026) and Qwen3-235B-A22B (2025) are frontier reasoning models from MistralAI and Alibaba. Mistral Medium 3.5 ships a 256K-token context window, while Qwen3-235B-A22B ships a 128K-token context window. On MMLU PRO, Qwen3-235B-A22B leads by 3 pts. On pricing, Qwen3-235B-A22B costs $0.4/1M input tokens versus $1.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen3-235B-A22B is ~275% cheaper at $0.4/1M; pay for Mistral Medium 3.5 only for reasoning depth.
Decision scorecard
Local evidence first| Signal | Mistral Medium 3.5 | Qwen3-235B-A22B |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Long context |
| Context window | 256K | 128K |
| Cheapest output | $7.5/1M tokens | $1.2/1M tokens |
| Provider routes | 2 tracked | 4 tracked |
| Shared benchmarks | 2 rows | MMLU PRO leader |
Decision tradeoffs
- Mistral Medium 3.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Mistral Medium 3.5 uniquely exposes Vision, Multimodal, and Reasoning in local model data.
- Local decision data tags Mistral Medium 3.5 for Coding, RAG, and Agents.
- Qwen3-235B-A22B leads the largest shared benchmark signal on MMLU PRO by 3 points.
- Qwen3-235B-A22B has the lower cheapest tracked output price at $1.2/1M tokens.
- Qwen3-235B-A22B has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Qwen3-235B-A22B for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Mistral Medium 3.5
$3,075
Cheapest tracked route: Mistral AI Studio
Qwen3-235B-A22B
$620
Cheapest tracked route: AWS Bedrock
Estimated monthly gap: $2,455. 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.
- Qwen3-235B-A22B is $6.3/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Reasoning before moving production traffic.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Mistral Medium 3.5 is $6.3/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Mistral Medium 3.5 adds Vision, Multimodal, and Reasoning in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-29 | 2025-04-29 |
| Context window | 256K | 128K |
| Parameters | 128B | 235B |
| Architecture | decoder only | decoder only |
| License | Mistral License | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Mistral Medium 3.5 | Qwen3-235B-A22B |
|---|---|---|
| Input price | $1.5/1M tokens | $0.4/1M tokens |
| Output price | $7.5/1M tokens | $1.2/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Medium 3.5 | Qwen3-235B-A22B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Mistral Medium 3.5 | Qwen3-235B-A22B |
|---|---|---|
| MMLU PRO | 79.8 | 82.8 |
| Google-Proof Q&A | 71.3 | 86.1 |
Deep dive
On shared benchmark coverage, MMLU PRO has Mistral Medium 3.5 at 79.8 and Qwen3-235B-A22B at 82.8, with Qwen3-235B-A22B ahead by 3 points; Google-Proof Q&A has Mistral Medium 3.5 at 71.3 and Qwen3-235B-A22B at 86.1, with Qwen3-235B-A22B ahead by 14.8 points. The largest visible gap is 14.8 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 vision: Mistral Medium 3.5, multimodal input: Mistral Medium 3.5, reasoning mode: Mistral Medium 3.5, function calling: Mistral Medium 3.5, and tool use: Mistral Medium 3.5. 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, Mistral Medium 3.5 lists $1.5/1M input and $7.5/1M output tokens, while Qwen3-235B-A22B lists $0.4/1M input and $1.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-235B-A22B lower by about $2.66 per million blended tokens. Availability is 2 providers versus 4, so concentration risk also matters.
Choose Mistral Medium 3.5 when reasoning depth and larger context windows are central to the workload. Choose Qwen3-235B-A22B when provider fit, lower input-token cost, and broader provider choice 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 Medium 3.5 or Qwen3-235B-A22B?
Mistral Medium 3.5 supports 256K tokens, while Qwen3-235B-A22B 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 Medium 3.5 or Qwen3-235B-A22B?
Qwen3-235B-A22B is cheaper on tracked token pricing. Mistral Medium 3.5 costs $1.5/1M input and $7.5/1M output tokens. Qwen3-235B-A22B costs $0.4/1M input and $1.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mistral Medium 3.5 or Qwen3-235B-A22B open source?
Mistral Medium 3.5 is listed under Mistral License. Qwen3-235B-A22B 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 Medium 3.5 or Qwen3-235B-A22B?
Mistral Medium 3.5 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Mistral Medium 3.5 or Qwen3-235B-A22B?
Mistral Medium 3.5 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 Medium 3.5 and Qwen3-235B-A22B?
Mistral Medium 3.5 is available on Mistral AI Studio and OpenRouter. Qwen3-235B-A22B is available on Fireworks AI, AWS Bedrock, OpenRouter, and Venice AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-20. Data sourced from public model cards and provider documentation.