Code Davinci 002 vs Kimi K2 Thinking Turbo
Code Davinci 002 (2021) and Kimi K2 Thinking Turbo (2025) compare a coding-specialized model against a standalone API model. Code Davinci 002 ships a not-yet-sourced context window, while Kimi K2 Thinking Turbo ships a 262k-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 002 is coding-specialized model, while Kimi K2 Thinking Turbo is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | Code Davinci 002 | Kimi K2 Thinking Turbo |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents and code generation | general production evaluation |
| Decision fit | Coding | Long context |
| Context window | — | 262k |
| Cheapest output | - | $8/1M tokens |
| Provider routes | 0 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Code Davinci 002 for Coding.
- Kimi K2 Thinking Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Kimi K2 Thinking Turbo has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Kimi K2 Thinking Turbo for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Code Davinci 002
Unavailable
No complete token price in local provider data
Kimi K2 Thinking Turbo
$2,920
Cheapest tracked route/tier: Vercel AI Gateway
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Code Davinci 002 and Kimi K2 Thinking Turbo; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Kimi K2 Thinking Turbo and Code Davinci 002; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2021-08-16 | 2025-11-06 |
| Context window | — | 262k |
| Parameters | — | 1T (32B active) |
| Architecture | decoder only | - |
| License | Proprietary | MIT(OSI) |
| Openness | Proprietary | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Code Davinci 002 | Kimi K2 Thinking Turbo |
|---|---|---|
| Input price | - | $1.15/1M tokens |
| Output price | - | $8/1M tokens |
| Providers | - |
Capabilities
| Capability | Code Davinci 002 | Kimi K2 Thinking Turbo |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
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 Kimi K2 Thinking Turbo has $1.15/1M 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 Kimi K2 Thinking Turbo 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 Kimi K2 Thinking Turbo open source?
Code Davinci 002 is listed under Proprietary. Kimi K2 Thinking Turbo 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 Kimi K2 Thinking Turbo?
Code Davinci 002 is available on the tracked providers still being sourced. Kimi K2 Thinking Turbo is available on Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Code Davinci 002 over Kimi K2 Thinking Turbo?
Treat this as a product-type comparison: Code Davinci 002 is coding-specialized model, while Kimi K2 Thinking Turbo is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive. 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 Kimi K2 Thinking Turbo.
What is the main difference between Code Davinci 002 and Kimi K2 Thinking Turbo?
Code Davinci 002 and Kimi K2 Thinking Turbo 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.
Continue comparing
Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.