Llama 3.2 1B vs Mixtral 8x22B Instruct v0.3
Llama 3.2 1B (2024) and Mixtral 8x22B Instruct v0.3 (2024) are compact production models from AI at Meta and MistralAI. Llama 3.2 1B ships a 128K-token context window, while Mixtral 8x22B Instruct v0.3 ships a 64K-token context window. On pricing, Llama 3.2 1B costs $0.1/1M input tokens versus $2/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B is ~1900% cheaper at $0.1/1M; pay for Mixtral 8x22B Instruct v0.3 only for provider fit.
Specs
| Released | 2024-09-25 | 2024-07-01 |
| 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 Instruct v0.3 | |
|---|---|---|
| Input price | $0.1/1M tokens | $2/1M tokens |
| Output price | $0.1/1M tokens | $2/1M tokens |
| Providers |
Capabilities
| Llama 3.2 1B | Mixtral 8x22B Instruct v0.3 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: Mixtral 8x22B Instruct v0.3. 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 lists $0.1/1M input and $0.1/1M output tokens, while Mixtral 8x22B Instruct v0.3 lists $2/1M input and $2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B lower by about $1.9 per million blended tokens. Availability is 1 providers versus 1, 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 Instruct v0.3 when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, Llama 3.2 1B or Mixtral 8x22B Instruct v0.3?
Llama 3.2 1B supports 128K tokens, while Mixtral 8x22B Instruct v0.3 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 Instruct v0.3?
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 Instruct v0.3 costs $2/1M input and $2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.2 1B or Mixtral 8x22B Instruct v0.3 open source?
Llama 3.2 1B is listed under Open Source. Mixtral 8x22B Instruct v0.3 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 function calling, Llama 3.2 1B or Mixtral 8x22B Instruct v0.3?
Mixtral 8x22B Instruct v0.3 has the clearer documented function calling signal in this comparison. If function calling 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 and Mixtral 8x22B Instruct v0.3?
Llama 3.2 1B is available on Fireworks AI. Mixtral 8x22B Instruct v0.3 is available on Replicate API. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick Llama 3.2 1B over Mixtral 8x22B Instruct v0.3?
Llama 3.2 1B is ~1900% cheaper at $0.1/1M; pay for Mixtral 8x22B Instruct v0.3 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 Instruct v0.3.
Continue comparing
Last reviewed: 2026-04-15. Data sourced from public model cards and provider documentation.