Llama 3.2 1B vs Mistral Large 2
Llama 3.2 1B (2024) and Mistral Large 2 (2025) are compact production models from AI at Meta and MistralAI. Llama 3.2 1B ships a 128K-token context window, while Mistral Large 2 ships a 128K-token context window. On HumanEval, Mistral Large 2 leads by 56.7 pts. On pricing, Llama 3.2 1B costs $0.1/1M input tokens versus $0.48/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B is ~380% cheaper at $0.1/1M; pay for Mistral Large 2 only for vision-heavy evaluation.
Specs
| Released | 2024-09-25 | 2025-11-25 |
| Context window | 128K | 128K |
| Parameters | 1.23B | 123B |
| Architecture | decoder only | decoder only |
| License | Open Source | True |
| Knowledge cutoff | 2023-12 | 2025-07 |
Pricing and availability
| Llama 3.2 1B | Mistral Large 2 | |
|---|---|---|
| Input price | $0.1/1M tokens | $0.48/1M tokens |
| Output price | $0.1/1M tokens | $2.4/1M tokens |
| Providers |
Capabilities
| Llama 3.2 1B | Mistral Large 2 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3.2 1B | Mistral Large 2 |
|---|---|---|
| HumanEval | 28.1 | 84.8 |
| Massive Multitask Language Understanding | 54.2 | 84.0 |
Deep dive
On shared benchmark coverage, HumanEval has Llama 3.2 1B at 28.1 and Mistral Large 2 at 84.8, with Mistral Large 2 ahead by 56.7 points; Massive Multitask Language Understanding has Llama 3.2 1B at 54.2 and Mistral Large 2 at 84, with Mistral Large 2 ahead by 29.8 points. The largest visible gap is 56.7 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 vision: Mistral Large 2, multimodal input: Mistral Large 2, function calling: Mistral Large 2, tool use: Mistral Large 2, and structured outputs: Mistral Large 2. 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 Mistral Large 2 lists $0.48/1M input and $2.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B lower by about $0.96 per million blended tokens. Availability is 1 providers versus 4, so concentration risk also matters.
Choose Llama 3.2 1B when provider fit and lower input-token cost are central to the workload. Choose Mistral Large 2 when vision-heavy evaluation 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 Mistral Large 2?
Llama 3.2 1B supports 128K tokens, while Mistral Large 2 supports 128K 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 Mistral Large 2?
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. Mistral Large 2 costs $0.48/1M input and $2.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.2 1B or Mistral Large 2 open source?
Llama 3.2 1B is listed under Open Source. Mistral Large 2 is listed under True. 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 vision, Llama 3.2 1B or Mistral Large 2?
Mistral Large 2 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Llama 3.2 1B or Mistral Large 2?
Mistral Large 2 has the clearer documented multimodal input signal in this comparison. If multimodal input 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 Mistral Large 2?
Llama 3.2 1B is available on Fireworks AI. Mistral Large 2 is available on OpenRouter, IBM watsonx, AWS Bedrock, and Mistral AI Studio. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.