Llama 2 70B Chat vs Mistral Large 3 675B Instruct
Llama 2 70B Chat (2023) and Mistral Large 3 675B Instruct (2025) are compact production models from AI at Meta and MistralAI. Llama 2 70B Chat ships a 4K-token context window, while Mistral Large 3 675B Instruct ships a 128K-token context window. On pricing, Llama 2 70B Chat costs $0.5/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Mistral Large 3 675B Instruct fits 32x more tokens; pick it for long-context work and Llama 2 70B Chat for tighter calls.
Specs
| Released | 2023-07-18 | 2025-12-01 |
| Context window | 4K | 128K |
| Parameters | 70B | 675B |
| Architecture | decoder only | decoder only |
| License | Open Source | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 2 70B Chat | Mistral Large 3 675B Instruct | |
|---|---|---|
| Input price | $0.5/1M tokens | $0.5/1M tokens |
| Output price | $1.5/1M tokens | $1.5/1M tokens |
| Providers |
Capabilities
| Llama 2 70B Chat | Mistral Large 3 675B Instruct | |
|---|---|---|
| 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 is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Llama 2 70B Chat lists $0.5/1M input and $1.5/1M output tokens, while Mistral Large 3 675B Instruct lists $0.5/1M input and $1.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 2 70B Chat lower by about $0 per million blended tokens. Availability is 14 providers versus 3, so concentration risk also matters.
Choose Llama 2 70B Chat when provider fit and broader provider choice are central to the workload. Choose Mistral Large 3 675B Instruct when long-context analysis and larger context windows 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 2 70B Chat or Mistral Large 3 675B Instruct?
Mistral Large 3 675B Instruct supports 128K tokens, while Llama 2 70B 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 70B Chat or Mistral Large 3 675B Instruct?
Llama 2 70B Chat is cheaper on tracked token pricing. Llama 2 70B Chat costs $0.5/1M input and $1.5/1M output tokens. Mistral Large 3 675B Instruct costs $0.5/1M input and $1.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 2 70B Chat or Mistral Large 3 675B Instruct open source?
Llama 2 70B Chat is listed under Open Source. Mistral Large 3 675B Instruct is listed under 1. 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 70B Chat or Mistral Large 3 675B Instruct?
Both Llama 2 70B Chat and Mistral Large 3 675B Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Llama 2 70B Chat and Mistral Large 3 675B Instruct?
Llama 2 70B Chat is available on Databricks Foundation Model Serving, Microsoft Foundry, GCP Vertex AI, Alibaba Cloud PAI-EAS, and AWS Bedrock. Mistral Large 3 675B Instruct is available on AWS Bedrock, NVIDIA NIM, and Mistral AI Studio. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 2 70B Chat over Mistral Large 3 675B Instruct?
Mistral Large 3 675B Instruct fits 32x more tokens; pick it for long-context work and Llama 2 70B Chat for tighter calls. If your workload also depends on provider fit, start with Llama 2 70B Chat; if it depends on long-context analysis, run the same evaluation with Mistral Large 3 675B Instruct.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.