Gemma 3 12B vs text-davinci
Gemma 3 12B (2026) and text-davinci (2022) are compact production models from Google DeepMind and OpenAI. Gemma 3 12B ships a 33K-token context window, while text-davinci ships a 4K-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 3 12B fits 8x more tokens; pick it for long-context work and text-davinci for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 3 12B | text-davinci |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | General |
| Context window | 33K | 4K |
| Cheapest output | $0.13/1M tokens | - |
| Provider routes | 3 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 3 12B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Gemma 3 12B has broader tracked provider coverage for fallback and procurement flexibility.
- Gemma 3 12B uniquely exposes Structured outputs in local model data.
- Local decision data tags Gemma 3 12B for Classification and JSON / Tool use.
- Use text-davinci when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 3 12B
$64.50
Cheapest tracked route: OpenRouter
text-davinci
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemma 3 12B and text-davinci; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for text-davinci and Gemma 3 12B; plan for SDK, billing, or endpoint changes.
- Gemma 3 12B adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-01-01 | 2022-01-27 |
| Context window | 33K | 4K |
| Parameters | — | 175B |
| Architecture | decoder only | decoder only |
| License | Open Source | Unknown |
| Knowledge cutoff | 2024-08 | 2021-06 |
Pricing and availability
| Pricing attribute | Gemma 3 12B | text-davinci |
|---|---|---|
| Input price | $0.04/1M tokens | - |
| Output price | $0.13/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Gemma 3 12B | text-davinci |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Gemma 3 12B. 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: Gemma 3 12B has $0.04/1M input tokens and text-davinci has no token price sourced yet. Provider availability is 3 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 3 12B when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose text-davinci when provider fit 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
Which has a larger context window, Gemma 3 12B or text-davinci?
Gemma 3 12B supports 33K tokens, while text-davinci supports 4K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 3 12B or text-davinci open source?
Gemma 3 12B is listed under Open Source. text-davinci is listed under Unknown. 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, Gemma 3 12B or text-davinci?
Gemma 3 12B 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 Gemma 3 12B and text-davinci?
Gemma 3 12B is available on AWS Bedrock, OpenRouter, and GCP Vertex AI. text-davinci is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3 12B over text-davinci?
Gemma 3 12B fits 8x more tokens; pick it for long-context work and text-davinci for tighter calls. If your workload also depends on long-context analysis, start with Gemma 3 12B; if it depends on provider fit, run the same evaluation with text-davinci.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.