Llama 3.1 70B Instruct vs Mixtral 8x7B
Llama 3.1 70B Instruct (2024) and Mixtral 8x7B (2023) are compact production models from AI at Meta and MistralAI. Llama 3.1 70B Instruct ships a 128K-token context window, while Mixtral 8x7B ships a 32K-token context window. On HumanEval, Llama 3.1 70B Instruct leads by 3.6 pts. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Mixtral 8x7B is ~167% cheaper at $0.15/1M; pay for Llama 3.1 70B Instruct only for long-context analysis.
Specs
| Released | 2024-07-23 | 2023-12-11 |
| Context window | 128K | 32K |
| Parameters | 70B | 8x7B |
| Architecture | decoder only | mixture of experts |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Llama 3.1 70B Instruct | Mixtral 8x7B | |
|---|---|---|
| Input price | $0.4/1M tokens | $0.15/1M tokens |
| Output price | $0.4/1M tokens | $0.45/1M tokens |
| Providers |
Capabilities
| Llama 3.1 70B Instruct | Mixtral 8x7B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3.1 70B Instruct | Mixtral 8x7B |
|---|---|---|
| HumanEval | 84.1 | 80.5 |
| Massive Multitask Language Understanding | 86.0 | 80.2 |
| HellaSwag | 94.2 | 90.9 |
Deep dive
On shared benchmark coverage, HumanEval has Llama 3.1 70B Instruct at 84.1 and Mixtral 8x7B at 80.5, with Llama 3.1 70B Instruct ahead by 3.6 points; Massive Multitask Language Understanding has Llama 3.1 70B Instruct at 86 and Mixtral 8x7B at 80.2, with Llama 3.1 70B Instruct ahead by 5.8 points; HellaSwag has Llama 3.1 70B Instruct at 94.2 and Mixtral 8x7B at 90.9, with Llama 3.1 70B Instruct ahead by 3.3 points. The largest visible gap is 5.8 points on Massive Multitask Language Understanding, 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 8x7B lists $0.15/1M input and $0.45/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x7B lower by about $0.16 per million blended tokens. Availability is 11 providers versus 18, so concentration risk also matters.
Choose Llama 3.1 70B Instruct when long-context analysis and larger context windows are central to the workload. Choose Mixtral 8x7B when provider fit, lower input-token cost, and broader provider choice 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 8x7B?
Llama 3.1 70B 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.
Which is cheaper, Llama 3.1 70B Instruct or Mixtral 8x7B?
Mixtral 8x7B is cheaper on tracked token pricing. Llama 3.1 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.1 70B Instruct or Mixtral 8x7B open source?
Llama 3.1 70B Instruct is listed under Open Source. Mixtral 8x7B 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 8x7B?
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 8x7B?
Llama 3.1 70B Instruct is available on OctoAI API, Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 70B Instruct over Mixtral 8x7B?
Mixtral 8x7B is ~167% cheaper at $0.15/1M; pay for Llama 3.1 70B Instruct only for long-context analysis. 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 8x7B.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.