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, both list $0.50/1M input and $1.50/1M output tokens on the cheapest tracked route. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Mistral Large 3 675B Instruct fits 32x more tokens; pick it for long-context work and Llama 2 70B Chat for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 2 70B Chat | Mistral Large 3 675B Instruct |
|---|---|---|
| Best for | provider-routed production | multimodal apps and provider-routed production |
| Decision fit | Classification and JSON / Tool use | Coding, RAG, and Agents |
| Context window | 4k | 128k |
| Cheapest output | $1.50/1M tokens | $1.50/1M tokens |
| Provider routes | 14 tracked | 5 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 2 70B Chat has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 2 70B Chat for Classification and JSON / Tool use.
- Mistral Large 3 675B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Mistral Large 3 675B Instruct uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Mistral Large 3 675B Instruct for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 2 70B Chat
$775
Cheapest tracked route/tier: Databricks Foundation Model Serving
Mistral Large 3 675B Instruct
$775
Cheapest tracked route/tier: AWS Bedrock
Estimated monthly gap: $0.00. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on AWS Bedrock, NVIDIA NIM, and Microsoft Foundry; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Mistral Large 3 675B Instruct adds Vision and Multimodal in local capability data.
- Provider overlap exists on Microsoft Foundry, AWS Bedrock, and NVIDIA NIM; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-07-18 | 2025-12-01 |
| Context window | 4k | 128k |
| Parameters | 70B | 675B |
| Architecture | decoder only | decoder only |
| License | Llama 2 Community | Mistral License |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Non-commercial only |
| Knowledge cutoff | - | 2024-11 |
Pricing and availability
| Pricing attribute | Llama 2 70B Chat | Mistral Large 3 675B Instruct |
|---|---|---|
| Input price | $0.50/1M tokens | $0.50/1M tokens |
| Output price | $1.50/1M tokens | $1.50/1M tokens |
| Providers |
Capabilities
| Capability | Llama 2 70B Chat | Mistral Large 3 675B Instruct |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Mistral Large 3 675B Instruct and multimodal input: Mistral Large 3 675B Instruct. Both models share structured outputs, 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 70B Chat lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider, while Mistral Large 3 675B Instruct lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend is tied on the cheapest tracked routes. Availability is 14 providers versus 5, 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?
Neither is cheaper on tracked token pricing. Both list $0.50/1M input and $1.50/1M output tokens on the cheapest tracked route. Provider discounts, batch pricing, or route-specific tiers 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 Llama 2 Community. Mistral Large 3 675B Instruct is listed under Mistral License. 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 2 70B Chat or Mistral Large 3 675B Instruct?
Mistral Large 3 675B Instruct 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 2 70B Chat or Mistral Large 3 675B Instruct?
Mistral Large 3 675B Instruct 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 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, Mistral AI Studio, Microsoft Foundry, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.