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Llama Guard 7B vs Mixtral 8x22B v0.1

Llama Guard 7B (2023) and Mixtral 8x22B v0.1 (2024) are compact production models from AI at Meta and MistralAI. Llama Guard 7B ships a 2K-token context window, while Mixtral 8x22B v0.1 ships a 64K-token context window. On pricing, Llama Guard 7B costs $0.2/1M input tokens versus $0.3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Llama Guard 7B is ~50% cheaper at $0.2/1M; pay for Mixtral 8x22B v0.1 only for long-context analysis.

Specs

Released2023-12-072024-04-17
Context window2K64K
Parameters7B8x22B
Architecturedecoder onlymixture of experts
LicenseOpen SourceApache 2.0
Knowledge cutoff--

Pricing and availability

Llama Guard 7BMixtral 8x22B v0.1
Input price$0.2/1M tokens$0.3/1M tokens
Output price$0.2/1M tokens$0.9/1M tokens
Providers

Capabilities

Llama Guard 7BMixtral 8x22B v0.1
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 structured outputs: Llama Guard 7B. 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 7B lists $0.2/1M input and $0.2/1M 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 Guard 7B lower by about $0.28 per million blended tokens. Availability is 3 providers versus 8, so concentration risk also matters.

Choose Llama Guard 7B when provider fit and lower input-token cost are central to the workload. Choose Mixtral 8x22B v0.1 when long-context analysis, larger context windows, 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 has a larger context window, Llama Guard 7B or Mixtral 8x22B v0.1?

Mixtral 8x22B v0.1 supports 64K tokens, while Llama Guard 7B supports 2K 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 Guard 7B or Mixtral 8x22B v0.1?

Llama Guard 7B is cheaper on tracked token pricing. Llama Guard 7B costs $0.2/1M input and $0.2/1M 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 Guard 7B or Mixtral 8x22B v0.1 open source?

Llama Guard 7B 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.

Which is better for structured outputs, Llama Guard 7B or Mixtral 8x22B v0.1?

Llama Guard 7B 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 Guard 7B and Mixtral 8x22B v0.1?

Llama Guard 7B is available on Cloudflare Workers AI, Together AI, and 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 Guard 7B over Mixtral 8x22B v0.1?

Llama Guard 7B is ~50% cheaper at $0.2/1M; pay for Mixtral 8x22B v0.1 only for long-context analysis. If your workload also depends on provider fit, start with Llama Guard 7B; if it depends on long-context analysis, 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.