LLM Reference

Code Davinci 002 vs Mistral Large 2

Code Davinci 002 (2021) and Mistral Large 2 (2025) compare a coding-specialized model against a standalone API model. Code Davinci 002 ships a not-yet-sourced context window, while Mistral Large 2 ships a 128k-token context window. This page treats the result as workflow and deployment fit, not a universal model winner.

Treat this as a product-type comparison: Code Davinci 002 is coding-specialized model, while Mistral Large 2 is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.

Decision scorecard

Local evidence first
SignalCode Davinci 002Mistral Large 2
Product typeCoding-specialized modelStandalone API model
Best forcustom coding agents and code generationmultimodal apps, tool-calling agents, and provider-routed production
Decision fitCodingCoding, RAG, and Agents
Context window128k
Cheapest output-$2.40/1M tokens
Provider routes0 tracked4 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Code Davinci 002 when...
  • Local decision data tags Code Davinci 002 for Coding.
Choose Mistral Large 2 when...
  • Mistral Large 2 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Mistral Large 2 has broader tracked provider coverage for fallback and procurement flexibility.
  • Mistral Large 2 uniquely exposes Vision, Multimodal, and Function calling in local model data.
  • Local decision data tags Mistral Large 2 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.

Code Davinci 002

Unavailable

No complete token price in local provider data

Mistral Large 2

$984

Cheapest tracked route/tier: AWS Bedrock

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Code Davinci 002 -> Mistral Large 2
  • No overlapping tracked provider route is sourced for Code Davinci 002 and Mistral Large 2; plan for SDK, billing, or endpoint changes.
  • Mistral Large 2 adds Vision, Multimodal, and Function calling in local capability data.
Mistral Large 2 -> Code Davinci 002
  • No overlapping tracked provider route is sourced for Mistral Large 2 and Code Davinci 002; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.

Specs

Specification
Released2021-08-162025-11-25
Context window128k
Parameters123B
Architecturedecoder onlydecoder only
LicenseProprietaryMistral License
OpennessProprietaryOpen weights
Commercial useCommercial use with conditionsNon-commercial only
Knowledge cutoff-2025-07

Pricing and availability

Pricing attributeCode Davinci 002Mistral Large 2
Input price-$0.48/1M tokens
Output price-$2.40/1M tokens
Providers-

Capabilities

CapabilityCode Davinci 002Mistral Large 2
VisionNoYes
MultimodalNoYes
ReasoningNoNo
Function callingNoYes
Tool useNoYes
Structured outputsNoYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

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 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, 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: 2026-05-11. Data sourced from public model cards and provider documentation.