Mixtral 8x22B v0.1 vs Qwen2.5-72B
Mixtral 8x22B v0.1 (2024) and Qwen2.5-72B (2025) are compact production models from MistralAI and Alibaba. Mixtral 8x22B v0.1 ships a 64k-token context window, while Qwen2.5-72B ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
Qwen2.5-72B is safer overall; choose Mixtral 8x22B v0.1 when provider fit matters.
Decision scorecard
Local evidence first| Signal | Mixtral 8x22B v0.1 | Qwen2.5-72B |
|---|---|---|
| Best for | provider-routed production | tool-calling agents |
| Decision fit | Coding and Classification | RAG, Agents, and Long context |
| Context window | 64k | 128k |
| Cheapest output | $0.65/1M tokens | - |
| Provider routes | 8 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Mixtral 8x22B v0.1 has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Mixtral 8x22B v0.1 for Coding and Classification.
- Qwen2.5-72B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen2.5-72B uniquely exposes Function calling and Tool use in local model data.
- Local decision data tags Qwen2.5-72B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Mixtral 8x22B v0.1
$683
Cheapest tracked route/tier: DeepInfra
Qwen2.5-72B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Mixtral 8x22B v0.1 and Qwen2.5-72B; plan for SDK, billing, or endpoint changes.
- Qwen2.5-72B adds Function calling and Tool use in local capability data.
- No overlapping tracked provider route is sourced for Qwen2.5-72B and Mixtral 8x22B v0.1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-04-17 | 2025-10-10 |
| Context window | 64k | 128k |
| Parameters | 8x22B | 72B |
| Architecture | mixture of experts | - |
| License | Apache 2.0(OSI) | Apache 2.0(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | 2024-01 | 2024-09 |
Pricing and availability
| Pricing attribute | Mixtral 8x22B v0.1 | Qwen2.5-72B |
|---|---|---|
| Input price | $0.65/1M tokens | - |
| Output price | $0.65/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Mixtral 8x22B v0.1 | Qwen2.5-72B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: Qwen2.5-72B and tool use: Qwen2.5-72B. Both models share the core language-model surface, 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.
Pricing coverage is uneven: Mixtral 8x22B v0.1 has $0.65/1M input tokens and Qwen2.5-72B has no token price sourced yet. Provider availability is 8 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Mixtral 8x22B v0.1 when provider fit and broader provider choice are central to the workload. Choose Qwen2.5-72B 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. 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Mixtral 8x22B v0.1 or Qwen2.5-72B?
Qwen2.5-72B supports 128k tokens, while Mixtral 8x22B v0.1 supports 64k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Mixtral 8x22B v0.1 or Qwen2.5-72B open source?
Mixtral 8x22B v0.1 is listed under Apache 2.0. Qwen2.5-72B 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 function calling, Mixtral 8x22B v0.1 or Qwen2.5-72B?
Qwen2.5-72B 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.
Which is better for tool use, Mixtral 8x22B v0.1 or Qwen2.5-72B?
Qwen2.5-72B has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Mixtral 8x22B v0.1 and Qwen2.5-72B?
Mixtral 8x22B v0.1 is available on NVIDIA NIM, OctoAI API (Deprecated), Fireworks AI, DeepInfra, and Baseten API. Qwen2.5-72B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Mixtral 8x22B v0.1 over Qwen2.5-72B?
Qwen2.5-72B is safer overall; choose Mixtral 8x22B v0.1 when provider fit matters. If your workload also depends on provider fit, start with Mixtral 8x22B v0.1; if it depends on long-context analysis, run the same evaluation with Qwen2.5-72B.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.