Llama 3.1 405B vs Mixtral 8x7B
Llama 3.1 405B (2024) and Mixtral 8x7B (2023) are compact production models from AI at Meta and MistralAI. Llama 3.1 405B ships a 128K-token context window, while Mixtral 8x7B ships a 32K-token context window. On Google-Proof Q&A, Mixtral 8x7B leads by 3.3 pts. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Llama 3.1 405B fits 4x more tokens; pick it for long-context work and Mixtral 8x7B for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 405B | Mixtral 8x7B |
|---|---|---|
| Decision fit | Coding, Long context, and Classification | Coding and Classification |
| Context window | 128K | 32K |
| Cheapest output | - | $0.45/1M tokens |
| Provider routes | 0 tracked | 18 tracked |
| Shared benchmarks | 4 rows | Google-Proof Q&A leader |
Decision tradeoffs
- Llama 3.1 405B leads the largest shared benchmark signal on HumanEval by 8.5 points.
- Llama 3.1 405B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Llama 3.1 405B for Coding, Long context, and Classification.
- Mixtral 8x7B leads the largest shared benchmark signal on Google-Proof Q&A by 3.3 points.
- Mixtral 8x7B has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Mixtral 8x7B for Coding and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 405B
Unavailable
No complete token price in local provider data
Mixtral 8x7B
$233
Cheapest tracked route: Mistral AI Studio
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.1 405B and Mixtral 8x7B; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Mixtral 8x7B and Llama 3.1 405B; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2023-12-11 |
| Context window | 128K | 32K |
| Parameters | 405B | 8x7B |
| Architecture | decoder only | mixture of experts |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2023-12 | 2023-12 |
Pricing and availability
| Pricing attribute | Llama 3.1 405B | Mixtral 8x7B |
|---|---|---|
| Input price | - | $0.15/1M tokens |
| Output price | - | $0.45/1M tokens |
| Providers | - |
Capabilities
| Capability | Llama 3.1 405B | Mixtral 8x7B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
Benchmarks
| Benchmark | Llama 3.1 405B | Mixtral 8x7B |
|---|---|---|
| Google-Proof Q&A | 51.5 | 54.8 |
| HumanEval | 89.0 | 80.5 |
| Massive Multitask Language Understanding | 88.6 | 80.2 |
| HellaSwag | 95.8 | 90.9 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Llama 3.1 405B at 51.5 and Mixtral 8x7B at 54.8, with Mixtral 8x7B ahead by 3.3 points; HumanEval has Llama 3.1 405B at 89 and Mixtral 8x7B at 80.5, with Llama 3.1 405B ahead by 8.5 points; Massive Multitask Language Understanding has Llama 3.1 405B at 88.6 and Mixtral 8x7B at 80.2, with Llama 3.1 405B ahead by 8.4 points. The largest visible gap is 8.5 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 is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
Pricing coverage is uneven: Llama 3.1 405B has no token price sourced yet and Mixtral 8x7B has $0.15/1M input tokens. Provider availability is 0 tracked routes versus 18. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 405B when long-context analysis and larger context windows are central to the workload. Choose Mixtral 8x7B when provider fit 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 405B or Mixtral 8x7B?
Llama 3.1 405B 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.
Is Llama 3.1 405B or Mixtral 8x7B open source?
Llama 3.1 405B 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.
Where can I run Llama 3.1 405B and Mixtral 8x7B?
Llama 3.1 405B is available on the tracked providers still being sourced. Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API (Deprecated). Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 405B over Mixtral 8x7B?
Llama 3.1 405B fits 4x more tokens; pick it for long-context work and Mixtral 8x7B for tighter calls. If your workload also depends on long-context analysis, start with Llama 3.1 405B; if it depends on provider fit, run the same evaluation with Mixtral 8x7B.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.