LLM ReferenceLLM Reference

Llama Guard 3 1B vs Mistral Large

Llama Guard 3 1B (2024) and Mistral Large (2024) are compact production models from AI at Meta and MistralAI. Llama Guard 3 1B ships a not-yet-sourced context window, while Mistral Large ships a 32k-token context window. On pricing, Llama Guard 3 1B costs $0.1/1M input tokens versus $0.32/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Llama Guard 3 1B is ~220% cheaper at $0.1/1M; pay for Mistral Large only for vision-heavy evaluation.

Specs

Released2024-09-252024-02-08
Context window32k
Parameters1B
Architecturedecoder only-
LicenseOpen SourceProprietary
Knowledge cutoff-2024-03

Pricing and availability

Llama Guard 3 1BMistral Large
Input price$0.1/1M tokens$0.32/1M tokens
Output price$0.1/1M tokens$0.96/1M tokens
Providers

Capabilities

Llama Guard 3 1BMistral Large
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 vision: Mistral Large, function calling: Mistral Large, tool use: Mistral Large, and structured outputs: Mistral Large. 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 Guard 3 1B lists $0.1/1M input and $0.1/1M output tokens, while Mistral Large lists $0.32/1M input and $0.96/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama Guard 3 1B lower by about $0.41 per million blended tokens. Availability is 1 providers versus 8, so concentration risk also matters.

Choose Llama Guard 3 1B when provider fit and lower input-token cost are central to the workload. Choose Mistral Large 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. 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 is cheaper, Llama Guard 3 1B or Mistral Large?

Llama Guard 3 1B is cheaper on tracked token pricing. Llama Guard 3 1B costs $0.1/1M input and $0.1/1M output tokens. Mistral Large costs $0.32/1M input and $0.96/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Llama Guard 3 1B or Mistral Large open source?

Llama Guard 3 1B is listed under Open Source. Mistral Large is listed under Proprietary. 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 Guard 3 1B or Mistral Large?

Mistral Large 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for function calling, Llama Guard 3 1B or Mistral Large?

Mistral Large 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.

Which is better for tool use, Llama Guard 3 1B or Mistral Large?

Mistral Large has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Llama Guard 3 1B and Mistral Large?

Llama Guard 3 1B is available on Fireworks AI. Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.