Mixtral 8x7B vs Qwen2.5-72B-Instruct
Mixtral 8x7B (2023) and Qwen2.5-72B-Instruct (2024) are compact production models from MistralAI and Alibaba. Mixtral 8x7B ships a 32k-token context window, while Qwen2.5-72B-Instruct ships a 128k-token context window. On Google-Proof Q&A, Mixtral 8x7B leads by 16.4 pts. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.18/1M 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.
Pick Mixtral 8x7B for reasoning; Qwen2.5-72B-Instruct is better when long-context analysis matters more.
Decision scorecard
Local evidence first| Signal | Mixtral 8x7B | Qwen2.5-72B-Instruct |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Coding and Classification | Coding, RAG, and Long context |
| Context window | 32k | 128k |
| Cheapest output | $0.45/1M tokens | $0.54/1M tokens |
| Provider routes | 18 tracked | 7 tracked |
| Shared benchmarks | Google-Proof Q&A leader | 4 rows |
Decision tradeoffs
- Mixtral 8x7B holds a shared-benchmark lead on Google-Proof Q&A, ahead by 16.4 points.
- Mixtral 8x7B has the lower cheapest tracked output price at $0.45/1M tokens.
- Mixtral 8x7B has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Mixtral 8x7B for Coding and Classification.
- Qwen2.5-72B-Instruct holds a shared-benchmark lead on HumanEval, ahead by 6.1 points.
- Qwen2.5-72B-Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen2.5-72B-Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Qwen2.5-72B-Instruct for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Mixtral 8x7B
$233
Cheapest tracked route/tier: Mistral AI Studio
Qwen2.5-72B-Instruct
$279
Cheapest tracked route/tier: Chutes AI
Estimated monthly gap: $46.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on DeepInfra, Fireworks AI, and SiliconFlow; start route-level A/B tests there.
- Qwen2.5-72B-Instruct is $0.09/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Qwen2.5-72B-Instruct adds Structured outputs in local capability data.
- Provider overlap exists on Fireworks AI, DeepInfra, and SiliconFlow; start route-level A/B tests there.
- Mixtral 8x7B is $0.09/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-12-11 | 2024-06-07 |
| Context window | 32k | 128k |
| Parameters | 8x7B | 72.7B |
| Architecture | mixture of experts | decoder only |
| License | Apache 2.0(OSI) | Apache 2.0(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Mixtral 8x7B | Qwen2.5-72B-Instruct |
|---|---|---|
| Input price | $0.15/1M tokens | $0.18/1M tokens |
| Output price | $0.45/1M tokens | $0.54/1M tokens |
| Providers |
Capabilities
| Capability | Mixtral 8x7B | Qwen2.5-72B-Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Mixtral 8x7B | Qwen2.5-72B-Instruct |
|---|---|---|
| Google-Proof Q&A | 54.8 | 38.4 |
| HumanEval | 80.5 | 86.6 |
| Massive Multitask Language Understanding | 80.2 | 88.2 |
| HellaSwag | 90.9 | 95.6 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Mixtral 8x7B at 54.8 and Qwen2.5-72B-Instruct at 38.4, with Mixtral 8x7B ahead by 16.4 points; HumanEval has Mixtral 8x7B at 80.5 and Qwen2.5-72B-Instruct at 86.6, with Qwen2.5-72B-Instruct ahead by 6.1 points; Massive Multitask Language Understanding has Mixtral 8x7B at 80.2 and Qwen2.5-72B-Instruct at 88.2, with Qwen2.5-72B-Instruct ahead by 8 points. The largest visible gap is 16.4 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 structured outputs: Qwen2.5-72B-Instruct. 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.
For cost, Mixtral 8x7B lists $0.15/1M input and $0.45/1M output tokens on the cheapest tracked provider, while Qwen2.5-72B-Instruct lists $0.18/1M input and $0.54/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x7B lower by about $0.05 per million blended tokens. Availability is 18 providers versus 7, so concentration risk also matters.
Choose Mixtral 8x7B when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen2.5-72B-Instruct 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, Mixtral 8x7B or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct supports 128k tokens, while Mixtral 8x7B 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, Mixtral 8x7B or Qwen2.5-72B-Instruct?
Mixtral 8x7B is cheaper on tracked token pricing. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Qwen2.5-72B-Instruct costs $0.18/1M input and $0.54/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x7B or Qwen2.5-72B-Instruct open source?
Mixtral 8x7B is listed under Apache 2.0. Qwen2.5-72B-Instruct 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 structured outputs, Mixtral 8x7B or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Mixtral 8x7B and Qwen2.5-72B-Instruct?
Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API (Deprecated). Qwen2.5-72B-Instruct is available on DeepInfra, OpenRouter, Fireworks AI, Novita AI, and Chutes AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Mixtral 8x7B over Qwen2.5-72B-Instruct?
Pick Mixtral 8x7B for reasoning; Qwen2.5-72B-Instruct is better when long-context analysis matters more. If your workload also depends on provider fit, start with Mixtral 8x7B; if it depends on long-context analysis, run the same evaluation with Qwen2.5-72B-Instruct.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.