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Llama 3.1 405B vs Mixtral 8x22B v0.1

Llama 3.1 405B (2024) and Mixtral 8x22B v0.1 (2024) are compact production models from AI at Meta and MistralAI. Llama 3.1 405B ships a 128K-token context window, while Mixtral 8x22B v0.1 ships a 64K-token context window. On Google-Proof Q&A, Mixtral 8x22B v0.1 leads by 8.6 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 is safer overall; choose Mixtral 8x22B v0.1 when provider fit matters.

Decision scorecard

Local evidence first
SignalLlama 3.1 405BMixtral 8x22B v0.1
Decision fitCoding, Long context, and ClassificationCoding and Classification
Context window128K64K
Cheapest output-$0.65/1M tokens
Provider routes0 tracked8 tracked
Shared benchmarks4 rowsGoogle-Proof Q&A leader

Decision tradeoffs

Choose Llama 3.1 405B when...
  • Llama 3.1 405B leads the largest shared benchmark signal on HumanEval by 2.8 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.
Choose Mixtral 8x22B v0.1 when...
  • Mixtral 8x22B v0.1 leads the largest shared benchmark signal on Google-Proof Q&A by 8.6 points.
  • Mixtral 8x22B v0.1 has broader tracked provider coverage for fallback and procurement flexibility.
  • Local decision data tags Mixtral 8x22B v0.1 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 8x22B v0.1

$683

Cheapest tracked route: DeepInfra

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Llama 3.1 405B -> Mixtral 8x22B v0.1
  • No overlapping tracked provider route is sourced for Llama 3.1 405B and Mixtral 8x22B v0.1; plan for SDK, billing, or endpoint changes.
Mixtral 8x22B v0.1 -> Llama 3.1 405B
  • No overlapping tracked provider route is sourced for Mixtral 8x22B v0.1 and Llama 3.1 405B; plan for SDK, billing, or endpoint changes.

Specs

Specification
Released2024-07-232024-04-17
Context window128K64K
Parameters405B8x22B
Architecturedecoder onlymixture of experts
LicenseOpen SourceApache 2.0
Knowledge cutoff2023-122024-01

Pricing and availability

Pricing attributeLlama 3.1 405BMixtral 8x22B v0.1
Input price-$0.65/1M tokens
Output price-$0.65/1M tokens
Providers-

Capabilities

CapabilityLlama 3.1 405BMixtral 8x22B v0.1
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsNoNo
Code executionNoNo

Benchmarks

BenchmarkLlama 3.1 405BMixtral 8x22B v0.1
Google-Proof Q&A51.560.1
HumanEval89.086.2
Massive Multitask Language Understanding88.684.5
HellaSwag95.893.8

Deep dive

On shared benchmark coverage, Google-Proof Q&A has Llama 3.1 405B at 51.5 and Mixtral 8x22B v0.1 at 60.1, with Mixtral 8x22B v0.1 ahead by 8.6 points; HumanEval has Llama 3.1 405B at 89 and Mixtral 8x22B v0.1 at 86.2, with Llama 3.1 405B ahead by 2.8 points; Massive Multitask Language Understanding has Llama 3.1 405B at 88.6 and Mixtral 8x22B v0.1 at 84.5, with Llama 3.1 405B ahead by 4.1 points. The largest visible gap is 8.6 points on Google-Proof Q&A, 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 8x22B v0.1 has $0.65/1M input tokens. Provider availability is 0 tracked routes versus 8. 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 8x22B v0.1 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 8x22B v0.1?

Llama 3.1 405B 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.

Is Llama 3.1 405B or Mixtral 8x22B v0.1 open source?

Llama 3.1 405B 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.

Where can I run Llama 3.1 405B and Mixtral 8x22B v0.1?

Llama 3.1 405B is available on the tracked providers still being sourced. Mixtral 8x22B v0.1 is available on NVIDIA NIM, OctoAI API (Deprecated), 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 over Mixtral 8x22B v0.1?

Llama 3.1 405B 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 405B; if it depends on provider fit, run the same evaluation with Mixtral 8x22B v0.1.

Continue comparing

Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.