Llama 2 13B Chat vs Mixtral 8x7B
Llama 2 13B Chat (2023) and Mixtral 8x7B (2023) are compact production models from AI at Meta and MistralAI. Llama 2 13B Chat ships a 4K-token context window, while Mixtral 8x7B ships a 32K-token context window. On Google-Proof Q&A, Mixtral 8x7B leads by 13 pts. On pricing, Llama 2 13B Chat costs $0.1/1M input tokens versus $0.15/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 2 13B Chat is ~50% cheaper at $0.1/1M; pay for Mixtral 8x7B only for long-context analysis.
Specs
| Released | 2023-07-18 | 2023-12-11 |
| Context window | 4K | 32K |
| Parameters | 13B | 8x7B |
| Architecture | decoder only | mixture of experts |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Llama 2 13B Chat | Mixtral 8x7B | |
|---|---|---|
| Input price | $0.1/1M tokens | $0.15/1M tokens |
| Output price | $0.5/1M tokens | $0.45/1M tokens |
| Providers |
Capabilities
| Llama 2 13B Chat | Mixtral 8x7B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 2 13B Chat | Mixtral 8x7B |
|---|---|---|
| Google-Proof Q&A | 41.8 | 54.8 |
| HumanEval | 59.3 | 80.5 |
| Massive Multitask Language Understanding | 71.2 | 80.2 |
| HellaSwag | 88.5 | 90.9 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Llama 2 13B Chat at 41.8 and Mixtral 8x7B at 54.8, with Mixtral 8x7B ahead by 13 points; HumanEval has Llama 2 13B Chat at 59.3 and Mixtral 8x7B at 80.5, with Mixtral 8x7B ahead by 21.2 points; Massive Multitask Language Understanding has Llama 2 13B Chat at 71.2 and Mixtral 8x7B at 80.2, with Mixtral 8x7B ahead by 9 points. The largest visible gap is 21.2 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 structured outputs: Llama 2 13B Chat. 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 2 13B Chat lists $0.1/1M input and $0.5/1M output tokens, while Mixtral 8x7B lists $0.15/1M input and $0.45/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 2 13B Chat lower by about $0.02 per million blended tokens. Availability is 12 providers versus 18, so concentration risk also matters.
Choose Llama 2 13B Chat when provider fit and lower input-token cost are central to the workload. Choose Mixtral 8x7B 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.
FAQ
Which has a larger context window, Llama 2 13B Chat or Mixtral 8x7B?
Mixtral 8x7B supports 32K tokens, while Llama 2 13B Chat supports 4K 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 2 13B Chat or Mixtral 8x7B?
Llama 2 13B Chat is cheaper on tracked token pricing. Llama 2 13B Chat costs $0.1/1M input and $0.5/1M output tokens. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 2 13B Chat or Mixtral 8x7B open source?
Llama 2 13B Chat is listed under Open Source. Mixtral 8x7B 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 2 13B Chat or Mixtral 8x7B?
Llama 2 13B Chat 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 2 13B Chat and Mixtral 8x7B?
Llama 2 13B Chat is available on Alibaba Cloud PAI-EAS, AWS Bedrock, Microsoft Foundry, GCP Vertex AI, and Cloudflare Workers AI. Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 2 13B Chat over Mixtral 8x7B?
Llama 2 13B Chat is ~50% cheaper at $0.1/1M; pay for Mixtral 8x7B only for long-context analysis. If your workload also depends on provider fit, start with Llama 2 13B Chat; if it depends on long-context analysis, run the same evaluation with Mixtral 8x7B.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.