Mistral Large vs Qwen3-30B-A3B
Mistral Large (2024) and Qwen3-30B-A3B (2025) are compact production models from MistralAI and Alibaba. Mistral Large ships a 32k-token context window, while Qwen3-30B-A3B ships a 128k-token context window. On pricing, Qwen3-30B-A3B costs $0.05/1M input tokens versus $0.32/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Qwen3-30B-A3B is ~527% cheaper at $0.05/1M; pay for Mistral Large only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and provider-routed production | provider-routed production |
| Decision fit | Agents, Vision, and Classification | RAG, Long context, and Classification |
| Context window | 32k | 128k |
| Cheapest output | $0.96/1M tokens | $0.34/1M tokens |
| Provider routes | 8 tracked | 6 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- 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.
- Qwen3-30B-A3B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3-30B-A3B has the lower cheapest tracked output price at $0.34/1M tokens.
- Local decision data tags Qwen3-30B-A3B for RAG, Long context, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Mistral Large
$496
Cheapest tracked route/tier: GCP Vertex AI
Qwen3-30B-A3B
$125
Cheapest tracked route/tier: Cloudflare Workers AI
Estimated monthly gap: $371. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, Fireworks AI, and AWS Bedrock; start route-level A/B tests there.
- Qwen3-30B-A3B is $0.63/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- 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 $0.63/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Mistral Large adds Vision, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-08 | 2025-04-28 |
| Context window | 32k | 128k |
| Parameters | 123B | 30B |
| Architecture | - | Mixture of Experts |
| License | Mistral License | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Commercial use | Commercial use: non-commercial | Commercial use: permitted |
| Knowledge cutoff | 2024-03 | - |
Pricing and availability
| Pricing attribute | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| Input price | $0.32/1M tokens | $0.05/1M tokens |
| Output price | $0.96/1M tokens | $0.34/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Large | Qwen3-30B-A3B |
|---|---|---|
| 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 |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available for this pair.
Deep dive
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 on the cheapest tracked provider, while Qwen3-30B-A3B lists $0.05/1M input and $0.34/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-30B-A3B lower by about $0.38 per million blended tokens. Availability is 8 providers versus 6, so concentration risk also matters.
Choose Mistral Large when vision-heavy evaluation and broader provider choice are central to the workload. Choose Qwen3-30B-A3B 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 or Qwen3-30B-A3B?
Qwen3-30B-A3B 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 Qwen3-30B-A3B?
Qwen3-30B-A3B is cheaper on tracked token pricing. Mistral Large costs $0.32/1M input and $0.96/1M output tokens. Qwen3-30B-A3B costs $0.05/1M input and $0.34/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mistral Large or Qwen3-30B-A3B open source?
Mistral Large is listed under Mistral License. Qwen3-30B-A3B 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 Qwen3-30B-A3B?
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 Qwen3-30B-A3B?
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 Qwen3-30B-A3B?
Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. Qwen3-30B-A3B is available on Cloudflare Workers AI, OpenRouter, Fireworks AI, AWS Bedrock, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-16. Data sourced from public model cards and provider documentation.