Llama 3.1 70B Instruct vs Mixtral 8x22B v0.1
Llama 3.1 70B Instruct (2024) and Mixtral 8x22B v0.1 (2024) are compact production models from AI at Meta and MistralAI. Llama 3.1 70B Instruct ships a 128K-token context window, while Mixtral 8x22B v0.1 ships a 64K-token context window. On HumanEval, Mixtral 8x22B v0.1 leads by 2.1 pts. On pricing, Mixtral 8x22B v0.1 costs $0.3/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.1 70B Instruct is safer overall; choose Mixtral 8x22B v0.1 when provider fit matters.
Specs
| Released | 2024-07-23 | 2024-04-17 |
| Context window | 128K | 64K |
| Parameters | 70B | 8x22B |
| Architecture | decoder only | mixture of experts |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3.1 70B Instruct | Mixtral 8x22B v0.1 | |
|---|---|---|
| Input price | $0.4/1M tokens | $0.3/1M tokens |
| Output price | $0.4/1M tokens | $0.9/1M tokens |
| Providers |
Capabilities
| Llama 3.1 70B Instruct | Mixtral 8x22B v0.1 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3.1 70B Instruct | Mixtral 8x22B v0.1 |
|---|---|---|
| HumanEval | 84.1 | 86.2 |
| Massive Multitask Language Understanding | 86.0 | 84.5 |
| HellaSwag | 94.2 | 93.8 |
Deep dive
On shared benchmark coverage, HumanEval has Llama 3.1 70B Instruct at 84.1 and Mixtral 8x22B v0.1 at 86.2, with Mixtral 8x22B v0.1 ahead by 2.1 points; Massive Multitask Language Understanding has Llama 3.1 70B Instruct at 86 and Mixtral 8x22B v0.1 at 84.5, with Llama 3.1 70B Instruct ahead by 1.5 points; HellaSwag has Llama 3.1 70B Instruct at 94.2 and Mixtral 8x22B v0.1 at 93.8, with Llama 3.1 70B Instruct ahead by 0.4 points. The largest visible gap is 2.1 points on HumanEval, 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: Llama 3.1 70B 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, Llama 3.1 70B Instruct lists $0.4/1M input and $0.4/1M output tokens, while Mixtral 8x22B v0.1 lists $0.3/1M input and $0.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 70B Instruct lower by about $0.08 per million blended tokens. Availability is 11 providers versus 8, so concentration risk also matters.
Choose Llama 3.1 70B Instruct when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Mixtral 8x22B v0.1 when provider fit 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.
FAQ
Which has a larger context window, Llama 3.1 70B Instruct or Mixtral 8x22B v0.1?
Llama 3.1 70B Instruct 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.
Which is cheaper, Llama 3.1 70B Instruct or Mixtral 8x22B v0.1?
Mixtral 8x22B v0.1 is cheaper on tracked token pricing. Llama 3.1 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Mixtral 8x22B v0.1 costs $0.3/1M input and $0.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.1 70B Instruct or Mixtral 8x22B v0.1 open source?
Llama 3.1 70B Instruct is listed under Open Source. Mixtral 8x22B v0.1 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, Llama 3.1 70B Instruct or Mixtral 8x22B v0.1?
Llama 3.1 70B 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 Llama 3.1 70B Instruct and Mixtral 8x22B v0.1?
Llama 3.1 70B Instruct is available on OctoAI API, Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Mixtral 8x22B v0.1 is available on NVIDIA NIM, OctoAI API, Fireworks AI, DeepInfra, and Baseten API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 70B Instruct over Mixtral 8x22B v0.1?
Llama 3.1 70B Instruct is safer overall; choose Mixtral 8x22B v0.1 when provider fit matters. If your workload also depends on long-context analysis, start with Llama 3.1 70B Instruct; if it depends on provider fit, run the same evaluation with Mixtral 8x22B v0.1.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.