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Code Davinci 002 vs Gemma 7B Instruct

Code Davinci 002 (2021) and Gemma 7B Instruct (2024) are agentic coding models from OpenAI and Google DeepMind. Code Davinci 002 ships a not-yet-sourced context window, while Gemma 7B Instruct ships a 8K-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.

Gemma 7B Instruct is safer overall; choose Code Davinci 002 when coding workflow support matters.

Specs

Released2021-08-162024-02-21
Context window—8K
Parameters—7B
Architecturedecoder onlydecoder only
LicenseProprietaryOpen Source
Knowledge cutoff-2023-04

Pricing and availability

Code Davinci 002Gemma 7B Instruct
Input price-$0.05/1M tokens
Output price-$0.25/1M tokens
Providers-
NVIDIA NIMFireworks AITogether AIGCP Vertex AICloudflare Workers AIAlibaba Cloud PAI-EAS

Capabilities

Code Davinci 002Gemma 7B 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 differs most on structured outputs: Gemma 7B Instruct. 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 Gemma 7B Instruct has $0.05/1M input tokens. Provider availability is 0 tracked routes versus 8. 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 Gemma 7B 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 Gemma 7B Instruct open source?

Code Davinci 002 is listed under Proprietary. Gemma 7B Instruct is listed under Open Source. 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 structured outputs, Code Davinci 002 or Gemma 7B Instruct?

Gemma 7B Instruct 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 002 and Gemma 7B Instruct?

Code Davinci 002 is available on the tracked providers still being sourced. Gemma 7B Instruct is available on NVIDIA NIM, Fireworks AI, Together AI, GCP Vertex AI, and Cloudflare Workers AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Code Davinci 002 over Gemma 7B Instruct?

Gemma 7B 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 Gemma 7B Instruct.

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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.