Llama 3.2 1B Instruct vs Mixtral 8x7B
Llama 3.2 1B Instruct (2024) and Mixtral 8x7B (2023) are compact production models from AI at Meta and MistralAI. Llama 3.2 1B Instruct ships a 128K-token context window, while Mixtral 8x7B ships a 32K-token context window. On Google-Proof Q&A, Mixtral 8x7B leads by 29.2 pts. On pricing, Llama 3.2 1B Instruct costs $0.03/1M input tokens versus $0.15/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B Instruct is ~456% cheaper at $0.03/1M; pay for Mixtral 8x7B only for provider fit.
Specs
| Released | 2024-09-25 | 2023-12-11 |
| Context window | 128K | 32K |
| Parameters | 1.23B | 8x7B |
| Architecture | decoder only | mixture of experts |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2023-12 | 2023-12 |
Pricing and availability
| Llama 3.2 1B Instruct | Mixtral 8x7B | |
|---|---|---|
| Input price | $0.03/1M tokens | $0.15/1M tokens |
| Output price | $0.2/1M tokens | $0.45/1M tokens |
| Providers |
Capabilities
| Llama 3.2 1B Instruct | Mixtral 8x7B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3.2 1B Instruct | Mixtral 8x7B |
|---|---|---|
| Google-Proof Q&A | 25.6 | 54.8 |
| HumanEval | 28.1 | 80.5 |
| Massive Multitask Language Understanding | 49.3 | 80.2 |
| HellaSwag | 78.9 | 90.9 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Llama 3.2 1B Instruct at 25.6 and Mixtral 8x7B at 54.8, with Mixtral 8x7B ahead by 29.2 points; HumanEval has Llama 3.2 1B Instruct at 28.1 and Mixtral 8x7B at 80.5, with Mixtral 8x7B ahead by 52.4 points; Massive Multitask Language Understanding has Llama 3.2 1B Instruct at 49.3 and Mixtral 8x7B at 80.2, with Mixtral 8x7B ahead by 30.9 points. The largest visible gap is 52.4 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 3.2 1B 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 3.2 1B Instruct lists $0.03/1M input and $0.2/1M 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 Llama 3.2 1B Instruct lower by about $0.16 per million blended tokens. Availability is 5 providers versus 18, so concentration risk also matters.
Choose Llama 3.2 1B Instruct when long-context analysis, larger context windows, and lower input-token cost 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 3.2 1B Instruct or Mixtral 8x7B?
Llama 3.2 1B 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 3.2 1B Instruct or Mixtral 8x7B?
Llama 3.2 1B Instruct is cheaper on tracked token pricing. Llama 3.2 1B Instruct costs $0.03/1M input and $0.2/1M 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 3.2 1B Instruct or Mixtral 8x7B open source?
Llama 3.2 1B 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 3.2 1B Instruct or Mixtral 8x7B?
Llama 3.2 1B 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 3.2 1B Instruct and Mixtral 8x7B?
Llama 3.2 1B Instruct is available on OpenRouter, Fireworks AI, NVIDIA NIM, Bitdeer AI, and AWS Bedrock. 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 3.2 1B Instruct over Mixtral 8x7B?
Llama 3.2 1B Instruct is ~456% cheaper at $0.03/1M; pay for Mixtral 8x7B only for provider fit. If your workload also depends on long-context analysis, start with Llama 3.2 1B Instruct; if it depends on provider fit, run the same evaluation with Mixtral 8x7B.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.