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Code Davinci 002 vs Mistral Large 2

Code Davinci 002 (2021) and Mistral Large 2 (2025) are agentic coding models from OpenAI and MistralAI. Code Davinci 002 ships a not-yet-sourced context window, while Mistral Large 2 ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.

Mistral Large 2 is safer overall; choose Code Davinci 002 when coding workflow support matters.

Specs

Released2021-08-162025-11-25
Context window128K
Parameters123B
Architecturedecoder onlydecoder only
LicenseProprietaryTrue
Knowledge cutoff-2025-07

Pricing and availability

Code Davinci 002Mistral Large 2
Input price-$0.48/1M tokens
Output price-$2.4/1M tokens
Providers-

Capabilities

Code Davinci 002Mistral Large 2
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 2, multimodal input: Mistral Large 2, function calling: Mistral Large 2, tool use: Mistral Large 2, and structured outputs: Mistral Large 2. 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.

Pricing coverage is uneven: Code Davinci 002 has no token price sourced yet and Mistral Large 2 has $0.48/1M input tokens. Provider availability is 0 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Code Davinci 002 when coding workflow support are central to the workload. Choose Mistral Large 2 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

Is Code Davinci 002 or Mistral Large 2 open source?

Code Davinci 002 is listed under Proprietary. Mistral Large 2 is listed under True. 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, Code Davinci 002 or Mistral Large 2?

Mistral Large 2 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, Code Davinci 002 or Mistral Large 2?

Mistral Large 2 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.

Which is better for function calling, Code Davinci 002 or Mistral Large 2?

Mistral Large 2 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, Code Davinci 002 or Mistral Large 2?

Mistral Large 2 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 Code Davinci 002 and Mistral Large 2?

Code Davinci 002 is available on the tracked providers still being sourced. Mistral Large 2 is available on OpenRouter, IBM watsonx, AWS Bedrock, and Mistral AI Studio. 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.