Code Davinci 002 vs GLM-5 Turbo
Code Davinci 002 (2021) and GLM-5 Turbo (2026) compare a coding-specialized model against a standalone API model. Code Davinci 002 ships a not-yet-sourced context window, while GLM-5 Turbo ships a 200k-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 GLM-5 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 | GLM-5 Turbo |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents and code generation | reasoning-heavy apps, tool-calling agents, and provider-routed production |
| Decision fit | Coding | RAG, Agents, and Long context |
| Context window | — | 200k |
| Cheapest output | - | $4/1M tokens |
| Provider routes | 0 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Code Davinci 002 for Coding.
- GLM-5 Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 Turbo has broader tracked provider coverage for fallback and procurement flexibility.
- GLM-5 Turbo uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GLM-5 Turbo for RAG, Agents, and 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
GLM-5 Turbo
$1,960
Cheapest tracked route/tier: OpenRouter
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 GLM-5 Turbo; plan for SDK, billing, or endpoint changes.
- GLM-5 Turbo adds Reasoning, Function calling, and Tool use in local capability data.
- No overlapping tracked provider route is sourced for GLM-5 Turbo and Code Davinci 002; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2021-08-16 | 2026-03-01 |
| Context window | — | 200k |
| Parameters | — | 744B total, 40B active |
| Architecture | decoder only | mixture of experts |
| License | Proprietary | Proprietary |
| Knowledge cutoff | - | 2025-11 |
Pricing and availability
| Pricing attribute | Code Davinci 002 | GLM-5 Turbo |
|---|---|---|
| Input price | - | $1.20/1M tokens |
| Output price | - | $4/1M tokens |
| Providers | - |
Capabilities
| Capability | Code Davinci 002 | GLM-5 Turbo |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| 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 differs most on reasoning mode: GLM-5 Turbo, function calling: GLM-5 Turbo, tool use: GLM-5 Turbo, and structured outputs: GLM-5 Turbo. 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 GLM-5 Turbo has $1.20/1M input tokens. Provider availability is 0 tracked routes versus 2. 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 GLM-5 Turbo when reasoning depth 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.
FAQ
Is Code Davinci 002 or GLM-5 Turbo open source?
Code Davinci 002 is listed under Proprietary. GLM-5 Turbo 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 reasoning mode, Code Davinci 002 or GLM-5 Turbo?
GLM-5 Turbo has the clearer documented reasoning mode signal in this comparison. If reasoning mode 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 002 or GLM-5 Turbo?
GLM-5 Turbo 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 tool use, Code Davinci 002 or GLM-5 Turbo?
GLM-5 Turbo has the clearer documented tool use signal in this comparison. If tool use 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 002 or GLM-5 Turbo?
GLM-5 Turbo 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 GLM-5 Turbo?
Code Davinci 002 is available on the tracked providers still being sourced. GLM-5 Turbo is available on OpenRouter and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.