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Code Davinci 002 vs Phi 3.5 MoE Instruct

Code Davinci 002 (2021) and Phi 3.5 MoE Instruct (2024) are agentic coding models from OpenAI and Microsoft Research. Code Davinci 002 ships a not-yet-sourced context window, while Phi 3.5 MoE Instruct 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.

Phi 3.5 MoE Instruct is safer overall; choose Code Davinci 002 when coding workflow support matters.

Specs

Released2021-08-162024-08-20
Context window—128K
Parameters—16x3.8B (42B, 6.6B active)
Architecturedecoder onlydecoder only
LicenseProprietaryMIT
Knowledge cutoff--

Pricing and availability

Code Davinci 002Phi 3.5 MoE Instruct
Input price-$0.5/1M tokens
Output price-$0.5/1M tokens
Providers-
Fireworks AI

Capabilities

Code Davinci 002Phi 3.5 MoE Instruct
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 is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.

Pricing coverage is uneven: Code Davinci 002 has no token price sourced yet and Phi 3.5 MoE Instruct has $0.5/1M 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 002 when coding workflow support are central to the workload. Choose Phi 3.5 MoE Instruct when provider fit 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 002 or Phi 3.5 MoE Instruct open source?

Code Davinci 002 is listed under Proprietary. Phi 3.5 MoE Instruct is listed under MIT. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Where can I run Code Davinci 002 and Phi 3.5 MoE Instruct?

Code Davinci 002 is available on the tracked providers still being sourced. Phi 3.5 MoE Instruct is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Code Davinci 002 over Phi 3.5 MoE Instruct?

Phi 3.5 MoE Instruct is safer overall; choose Code Davinci 002 when coding workflow support matters. If your workload also depends on coding workflow support, start with Code Davinci 002; if it depends on provider fit, run the same evaluation with Phi 3.5 MoE Instruct.

What is the main difference between Code Davinci 002 and Phi 3.5 MoE Instruct?

Code Davinci 002 and Phi 3.5 MoE Instruct differ most on context, provider coverage, capabilities, or pricing depending on the data currently sourced. Use the specs table first, then validate the model behavior with your own prompts.

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Last reviewed: 2026-04-18. Data sourced from public model cards and provider documentation.