Llama 3 8B Instruct vs Mixtral 8x22B v0.1
Llama 3 8B Instruct (2024) and Mixtral 8x22B v0.1 (2024) are compact production models from AI at Meta and MistralAI. Llama 3 8B Instruct ships a 8K-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 15.3 pts. On pricing, Llama 3 8B Instruct costs $0.03/1M input tokens versus $0.3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3 8B Instruct is ~900% cheaper at $0.03/1M; pay for Mixtral 8x22B v0.1 only for long-context analysis.
Specs
| Released | 2024-04-18 | 2024-04-17 |
| Context window | 8K | 64K |
| Parameters | 8B | 8x22B |
| Architecture | decoder only | mixture of experts |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3 8B Instruct | Mixtral 8x22B v0.1 | |
|---|---|---|
| Input price | $0.03/1M tokens | $0.3/1M tokens |
| Output price | $0.04/1M tokens | $0.9/1M tokens |
| Providers |
Capabilities
| Llama 3 8B Instruct | Mixtral 8x22B v0.1 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3 8B Instruct | Mixtral 8x22B v0.1 |
|---|---|---|
| Google-Proof Q&A | 44.8 | 60.1 |
| HumanEval | 68.2 | 86.2 |
| Massive Multitask Language Understanding | 76.9 | 84.5 |
| HellaSwag | 91.1 | 93.8 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Llama 3 8B Instruct at 44.8 and Mixtral 8x22B v0.1 at 60.1, with Mixtral 8x22B v0.1 ahead by 15.3 points; HumanEval has Llama 3 8B Instruct at 68.2 and Mixtral 8x22B v0.1 at 86.2, with Mixtral 8x22B v0.1 ahead by 18 points; Massive Multitask Language Understanding has Llama 3 8B Instruct at 76.9 and Mixtral 8x22B v0.1 at 84.5, with Mixtral 8x22B v0.1 ahead by 7.6 points. The largest visible gap is 18 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 8B 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 8B Instruct lists $0.03/1M input and $0.04/1M 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 8B Instruct lower by about $0.45 per million blended tokens. Availability is 17 providers versus 8, so concentration risk also matters.
Choose Llama 3 8B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Mixtral 8x22B v0.1 when long-context analysis and larger context windows 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 8B Instruct or Mixtral 8x22B v0.1?
Mixtral 8x22B v0.1 supports 64K tokens, while Llama 3 8B Instruct supports 8K 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 8B Instruct or Mixtral 8x22B v0.1?
Llama 3 8B Instruct is cheaper on tracked token pricing. Llama 3 8B Instruct costs $0.03/1M input and $0.04/1M 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 8B Instruct or Mixtral 8x22B v0.1 open source?
Llama 3 8B 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 8B Instruct or Mixtral 8x22B v0.1?
Llama 3 8B 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 8B Instruct and Mixtral 8x22B v0.1?
Llama 3 8B Instruct is available on AWS Bedrock, DeepInfra, OctoAI API, Fireworks AI, and Alibaba Cloud PAI-EAS. 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 8B Instruct over Mixtral 8x22B v0.1?
Llama 3 8B Instruct is ~900% cheaper at $0.03/1M; pay for Mixtral 8x22B v0.1 only for long-context analysis. If your workload also depends on provider fit, start with Llama 3 8B Instruct; if it depends on long-context analysis, 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.