Llama 3.1 405B Instruct vs Mixtral 8x22B v0.1
Llama 3.1 405B Instruct (2024) and Mixtral 8x22B v0.1 (2024) are compact production models from AI at Meta and MistralAI. Llama 3.1 405B Instruct ships a 128K-token context window, while Mixtral 8x22B v0.1 ships a 64K-token context window. On Massive Multitask Language Understanding, Llama 3.1 405B Instruct leads by 4.1 pts. On pricing, Mixtral 8x22B v0.1 costs $0.3/1M input tokens versus $2.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Mixtral 8x22B v0.1 is ~700% cheaper at $0.3/1M; pay for Llama 3.1 405B Instruct only for long-context analysis.
Specs
| Released | 2024-07-23 | 2024-04-17 |
| Context window | 128K | 64K |
| Parameters | 405B | 8x22B |
| Architecture | decoder only | mixture of experts |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3.1 405B Instruct | Mixtral 8x22B v0.1 | |
|---|---|---|
| Input price | $2.4/1M tokens | $0.3/1M tokens |
| Output price | $2.4/1M tokens | $0.9/1M tokens |
| Providers |
Capabilities
| Llama 3.1 405B Instruct | Mixtral 8x22B v0.1 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3.1 405B Instruct | Mixtral 8x22B v0.1 |
|---|---|---|
| Massive Multitask Language Understanding | 88.6 | 84.5 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has Llama 3.1 405B Instruct at 88.6 and Mixtral 8x22B v0.1 at 84.5, with Llama 3.1 405B Instruct ahead by 4.1 points. The largest visible gap is 4.1 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 405B 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 405B Instruct lists $2.4/1M input and $2.4/1M 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 Mixtral 8x22B v0.1 lower by about $1.92 per million blended tokens. Availability is 11 providers versus 8, so concentration risk also matters.
Choose Llama 3.1 405B 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 405B Instruct or Mixtral 8x22B v0.1?
Llama 3.1 405B 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 405B Instruct or Mixtral 8x22B v0.1?
Mixtral 8x22B v0.1 is cheaper on tracked token pricing. Llama 3.1 405B Instruct costs $2.4/1M input and $2.4/1M 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 405B Instruct or Mixtral 8x22B v0.1 open source?
Llama 3.1 405B 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 405B Instruct or Mixtral 8x22B v0.1?
Llama 3.1 405B 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 405B Instruct and Mixtral 8x22B v0.1?
Llama 3.1 405B Instruct is available on OctoAI API, Together AI, Fireworks AI, IBM watsonx, and Scale AI GenAI Platform. 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 405B Instruct over Mixtral 8x22B v0.1?
Mixtral 8x22B v0.1 is ~700% cheaper at $0.3/1M; pay for Llama 3.1 405B Instruct only for long-context analysis. If your workload also depends on long-context analysis, start with Llama 3.1 405B Instruct; if it depends on provider fit, run the same evaluation with Mixtral 8x22B v0.1.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.