Mistral Large vs Qwen3-235B-A22B
Mistral Large (2024) and Qwen3-235B-A22B (2025) are compact production models from MistralAI and Alibaba. Mistral Large ships a 32k-token context window, while Qwen3-235B-A22B ships a 128K-token context window. On MMLU PRO, Qwen3-235B-A22B leads by 31.3 pts. On pricing, Mistral Large costs $0.32/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen3-235B-A22B fits 4x more tokens; pick it for long-context work and Mistral Large for tighter calls.
Decision scorecard
Local evidence first| Signal | Mistral Large | Qwen3-235B-A22B |
|---|---|---|
| Decision fit | Agents, Vision, and Classification | Coding, RAG, and Long context |
| Context window | 32k | 128K |
| Cheapest output | $0.96/1M tokens | $1.2/1M tokens |
| Provider routes | 8 tracked | 4 tracked |
| Shared benchmarks | 1 rows | MMLU PRO leader |
Decision tradeoffs
- Mistral Large has the lower cheapest tracked output price at $0.96/1M tokens.
- Mistral Large has broader tracked provider coverage for fallback and procurement flexibility.
- Mistral Large uniquely exposes Vision, Function calling, and Tool use in local model data.
- Local decision data tags Mistral Large for Agents, Vision, and Classification.
- Qwen3-235B-A22B leads the largest shared benchmark signal on MMLU PRO by 31.3 points.
- Qwen3-235B-A22B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- 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 Large
$496
Cheapest tracked route: GCP Vertex AI
Qwen3-235B-A22B
$620
Cheapest tracked route: AWS Bedrock
Estimated monthly gap: $124. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, AWS Bedrock, and OpenRouter; start route-level A/B tests there.
- Qwen3-235B-A22B is $0.24/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision, Function calling, and Tool use before moving production traffic.
- Provider overlap exists on AWS Bedrock, OpenRouter, and Fireworks AI; start route-level A/B tests there.
- Mistral Large is $0.24/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Mistral Large adds Vision, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-08 | 2025-04-29 |
| Context window | 32k | 128K |
| Parameters | — | 235B |
| Architecture | - | decoder only |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | 2024-03 | - |
Pricing and availability
| Pricing attribute | Mistral Large | Qwen3-235B-A22B |
|---|---|---|
| Input price | $0.32/1M tokens | $0.4/1M tokens |
| Output price | $0.96/1M tokens | $1.2/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Large | Qwen3-235B-A22B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Mistral Large | Qwen3-235B-A22B |
|---|---|---|
| MMLU PRO | 51.5 | 82.8 |
Deep dive
On shared benchmark coverage, MMLU PRO has Mistral Large at 51.5 and Qwen3-235B-A22B at 82.8, with Qwen3-235B-A22B ahead by 31.3 points. The largest visible gap is 31.3 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 vision: Mistral Large, function calling: Mistral Large, and tool use: Mistral Large. 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 Large lists $0.32/1M input and $0.96/1M 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 Mistral Large lower by about $0.13 per million blended tokens. Availability is 8 providers versus 4, so concentration risk also matters.
Choose Mistral Large when vision-heavy evaluation, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen3-235B-A22B when long-context analysis and larger context windows 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 Qwen3-235B-A22B?
Qwen3-235B-A22B supports 128K 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 Qwen3-235B-A22B?
Mistral Large is cheaper on tracked token pricing. Mistral Large costs $0.32/1M input and $0.96/1M 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 Large or Qwen3-235B-A22B open source?
Mistral Large is listed under Proprietary. 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 Large or Qwen3-235B-A22B?
Mistral Large 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for function calling, Mistral Large or Qwen3-235B-A22B?
Mistral Large has the clearer documented function calling signal in this comparison. If function calling 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 Qwen3-235B-A22B?
Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. 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.
Continue comparing
Last reviewed: 2026-05-20. Data sourced from public model cards and provider documentation.