LLM Reference

Code Davinci 001 vs GPT-4

Code Davinci 001 (2021) and GPT-4 (2023) compare a coding-specialized model against a standalone API model. Code Davinci 001 ships a not-yet-sourced context window, while GPT-4 ships a 8k-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 001 is coding-specialized model, while GPT-4 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 001GPT-4
Product typeCoding-specialized modelStandalone API model
Best forcustom coding agents and code generationmultimodal apps, tool-calling agents, and provider-routed production
Decision fitCodingCoding, Agents, and Vision
Context window8k
Cheapest output-$60/1M tokens
Provider routes0 tracked4 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Code Davinci 001 when...
  • Local decision data tags Code Davinci 001 for Coding.
Choose GPT-4 when...
  • GPT-4 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • GPT-4 has broader tracked provider coverage for fallback and procurement flexibility.
  • GPT-4 uniquely exposes Vision, Multimodal, and Function calling in local model data.
  • Local decision data tags GPT-4 for Coding, Agents, and Vision.

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-4

$39,000

Cheapest tracked route/tier: OpenAI API

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

Switch friction

Code Davinci 001 -> GPT-4
  • No overlapping tracked provider route is sourced for Code Davinci 001 and GPT-4; plan for SDK, billing, or endpoint changes.
  • GPT-4 adds Vision, Multimodal, and Function calling in local capability data.
GPT-4 -> Code Davinci 001
  • No overlapping tracked provider route is sourced for GPT-4 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
Released2021-07-012023-03-14
Context window8k
Parameters1.76T (8x222B MoE)*
Architecturedecoder onlymixture of experts
LicenseProprietaryProprietary
OpennessProprietaryProprietary
Commercial useCommercial use with conditionsCommercial use with conditions
Knowledge cutoff-2021-09

Pricing and availability

Pricing attributeCode Davinci 001GPT-4
Input price-$30/1M tokens
Output price-$60/1M tokens
Providers-

Capabilities

CapabilityCode Davinci 001GPT-4
VisionNoYes
MultimodalNoYes
ReasoningNoNo
Function callingNoYes
Tool useNoNo
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-4, multimodal input: GPT-4, function calling: GPT-4, structured outputs: GPT-4, and code execution: GPT-4. 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-4 has $30/1M 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 001 when coding workflow support are central to the workload. Choose GPT-4 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Is Code Davinci 001 or GPT-4 open source?

Code Davinci 001 is listed under Proprietary. GPT-4 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-4?

GPT-4 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-4?

GPT-4 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-4?

GPT-4 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 structured outputs, Code Davinci 001 or GPT-4?

GPT-4 has the clearer documented structured outputs signal in this comparison. If structured outputs 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-4?

Code Davinci 001 is available on the tracked providers still being sourced. GPT-4 is available on OpenAI API, Azure OpenAI, Salesforce Einstein Generative AI, and OpenRouter. 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.