Code Cushman 002 vs GLM-4.7
Code Cushman 002 (2021) and GLM-4.7 (2025) are agentic coding models from OpenAI and Tsinghua Knowledge Engineering Group (THUDM). Code Cushman 002 ships a not-yet-sourced context window, while GLM-4.7 ships a not-yet-sourced 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.
GLM-4.7 is safer overall; choose Code Cushman 002 when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | Code Cushman 002 | GLM-4.7 |
|---|---|---|
| Decision fit | Coding | Classification and JSON / Tool use |
| Context window | — | — |
| Cheapest output | - | $1.74/1M tokens |
| Provider routes | 0 tracked | 4 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Code Cushman 002 for Coding.
- GLM-4.7 has broader tracked provider coverage for fallback and procurement flexibility.
- GLM-4.7 uniquely exposes Structured outputs in local model data.
- Local decision data tags GLM-4.7 for Classification and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Code Cushman 002
Unavailable
No complete token price in local provider data
GLM-4.7
$739
Cheapest tracked route: 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 Cushman 002 and GLM-4.7; plan for SDK, billing, or endpoint changes.
- GLM-4.7 adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for GLM-4.7 and Code Cushman 002; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2021-11-15 | 2025-01-01 |
| Context window | — | — |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Code Cushman 002 | GLM-4.7 |
|---|---|---|
| Input price | - | $0.38/1M tokens |
| Output price | - | $1.74/1M tokens |
| Providers | - |
Capabilities
| Capability | Code Cushman 002 | GLM-4.7 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| 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: GLM-4.7. 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 Cushman 002 has no token price sourced yet and GLM-4.7 has $0.38/1M input tokens. Provider availability is 0 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Code Cushman 002 when coding workflow support are central to the workload. Choose GLM-4.7 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 Cushman 002 or GLM-4.7 open source?
Code Cushman 002 is listed under Proprietary. GLM-4.7 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, Code Cushman 002 or GLM-4.7?
GLM-4.7 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 Cushman 002 and GLM-4.7?
Code Cushman 002 is available on the tracked providers still being sourced. GLM-4.7 is available on Fireworks AI, GCP Vertex AI, NVIDIA NIM, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Code Cushman 002 over GLM-4.7?
GLM-4.7 is safer overall; choose Code Cushman 002 when coding workflow support matters. If your workload also depends on coding workflow support, start with Code Cushman 002; if it depends on provider fit, run the same evaluation with GLM-4.7.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.