LLM Reference

Code Davinci 001 vs GPT-5.1 Codex

Code Davinci 001 (2021) and GPT-5.1 Codex (2025) are agentic coding models from OpenAI. Code Davinci 001 ships a not-yet-sourced context window, while GPT-5.1 Codex ships a 400k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.

GPT-5.1 Codex is safer overall; choose Code Davinci 001 when coding workflow support matters.

Decision scorecard

Local evidence first
SignalCode Davinci 001GPT-5.1 Codex
Best forcustom coding agents and code generationcustom coding agents, code generation, and tool loops
Decision fitCodingCoding, RAG, and Agents
Context window400k
Cheapest output-$10/1M tokens
Provider routes0 tracked1 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Code Davinci 001 when...
  • Local decision data tags Code Davinci 001 for Coding.
Choose GPT-5.1 Codex when...
  • GPT-5.1 Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • GPT-5.1 Codex has broader tracked provider coverage for fallback and procurement flexibility.
  • GPT-5.1 Codex uniquely exposes Vision, Multimodal, and Function calling in local model data.
  • Local decision data tags GPT-5.1 Codex 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 001

Unavailable

No complete token price in local provider data

GPT-5.1 Codex

$3,500

Cheapest tracked route/tier: Vercel AI Gateway

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

Switch friction

Code Davinci 001 -> GPT-5.1 Codex
  • No overlapping tracked provider route is sourced for Code Davinci 001 and GPT-5.1 Codex; plan for SDK, billing, or endpoint changes.
  • GPT-5.1 Codex adds Vision, Multimodal, and Function calling in local capability data.
GPT-5.1 Codex -> Code Davinci 001
  • No overlapping tracked provider route is sourced for GPT-5.1 Codex and Code Davinci 001; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.

Specs

Specification
Released2021-07-012025-12-01
Context window400k
Parameters
Architecturedecoder onlydecoder only
LicenseProprietaryProprietary
OpennessProprietaryProprietary
Commercial useCommercial use with conditionsCommercial use with conditions
Knowledge cutoff-2024-09

Pricing and availability

Pricing attributeCode Davinci 001GPT-5.1 Codex
Input price-$1.25/1M tokens
Output price-$10/1M tokens
Providers-

Capabilities

CapabilityCode Davinci 001GPT-5.1 Codex
VisionNoYes
MultimodalNoYes
ReasoningNoNo
Function callingNoYes
Tool useNoYes
Structured outputsNoYes
Code executionNoYes
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: GPT-5.1 Codex, multimodal input: GPT-5.1 Codex, function calling: GPT-5.1 Codex, tool use: GPT-5.1 Codex, structured outputs: GPT-5.1 Codex, and code execution: GPT-5.1 Codex. 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 001 has no token price sourced yet and GPT-5.1 Codex has $1.25/1M input tokens. Provider availability is 0 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Code Davinci 001 when coding workflow support are central to the workload. Choose GPT-5.1 Codex when coding workflow support 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 001 or GPT-5.1 Codex open source?

Code Davinci 001 is listed under Proprietary. GPT-5.1 Codex is listed under Proprietary. 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 001 or GPT-5.1 Codex?

GPT-5.1 Codex 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for multimodal input, Code Davinci 001 or GPT-5.1 Codex?

GPT-5.1 Codex 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 001 or GPT-5.1 Codex?

GPT-5.1 Codex 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 001 or GPT-5.1 Codex?

GPT-5.1 Codex 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 001 and GPT-5.1 Codex?

Code Davinci 001 is available on the tracked providers still being sourced. GPT-5.1 Codex is available on Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.