Llama 3.2 1B vs Mixtral 8x22B v0.1
Llama 3.2 1B (2024) and Mixtral 8x22B v0.1 (2024) are compact production models from AI at Meta and MistralAI. Llama 3.2 1B ships a 128K-token context window, while Mixtral 8x22B v0.1 ships a 64K-token context window. On HumanEval, Mixtral 8x22B v0.1 leads by 58.1 pts. On pricing, Llama 3.2 1B costs $0.1/1M input tokens versus $0.3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B is ~200% cheaper at $0.1/1M; pay for Mixtral 8x22B v0.1 only for provider fit.
Specs
| Released | 2024-09-25 | 2024-04-17 |
| Context window | 128K | 64K |
| Parameters | 1.23B | 8x22B |
| Architecture | decoder only | mixture of experts |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Llama 3.2 1B | Mixtral 8x22B v0.1 | |
|---|---|---|
| Input price | $0.1/1M tokens | $0.3/1M tokens |
| Output price | $0.1/1M tokens | $0.9/1M tokens |
| Providers |
Capabilities
| Llama 3.2 1B | Mixtral 8x22B v0.1 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3.2 1B | Mixtral 8x22B v0.1 |
|---|---|---|
| HumanEval | 28.1 | 86.2 |
| Massive Multitask Language Understanding | 54.2 | 84.5 |
Deep dive
On shared benchmark coverage, HumanEval has Llama 3.2 1B at 28.1 and Mixtral 8x22B v0.1 at 86.2, with Mixtral 8x22B v0.1 ahead by 58.1 points; Massive Multitask Language Understanding has Llama 3.2 1B at 54.2 and Mixtral 8x22B v0.1 at 84.5, with Mixtral 8x22B v0.1 ahead by 30.3 points. The largest visible gap is 58.1 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.
For cost, Llama 3.2 1B lists $0.1/1M input and $0.1/1M 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 3.2 1B lower by about $0.38 per million blended tokens. Availability is 1 providers versus 8, so concentration risk also matters.
Choose Llama 3.2 1B when long-context analysis, larger context windows, and lower input-token cost 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 3.2 1B or Mixtral 8x22B v0.1?
Llama 3.2 1B 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 3.2 1B or Mixtral 8x22B v0.1?
Llama 3.2 1B is cheaper on tracked token pricing. Llama 3.2 1B costs $0.1/1M input and $0.1/1M 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 3.2 1B or Mixtral 8x22B v0.1 open source?
Llama 3.2 1B 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 3.2 1B and Mixtral 8x22B v0.1?
Llama 3.2 1B is available on Fireworks AI. 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 3.2 1B over Mixtral 8x22B v0.1?
Llama 3.2 1B is ~200% cheaper at $0.1/1M; pay for Mixtral 8x22B v0.1 only for provider fit. If your workload also depends on long-context analysis, start with Llama 3.2 1B; 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.