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 shared |
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 route 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 2.0OSI-approved | Apache 2.0OSI-approved |
| Openness | Open source | Open source |
| Weights | Unknown | Available |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: permitted | Commercial use: permitted |
| 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 |
| JSON / 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 |
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Last reviewed: 2026-07-11. Data sourced from public model cards and provider documentation.