GLM-4 Code 9B vs GPT-5.3-Codex
GLM-4 Code 9B (2025) and GPT-5.3-Codex (2026) are agentic coding models from Tsinghua Knowledge Engineering Group (THUDM) and OpenAI. GLM-4 Code 9B ships a not-yet-sourced context window, while GPT-5.3-Codex ships a 400K-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.
GPT-5.3-Codex is safer overall; choose GLM-4 Code 9B when provider fit matters.
Decision scorecard
Local evidence first| Signal | GLM-4 Code 9B | GPT-5.3-Codex |
|---|---|---|
| Decision fit | General | Coding, RAG, and Agents |
| Context window | — | 400K |
| Cheapest output | - | $14/1M tokens |
| Provider routes | 0 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use GLM-4 Code 9B when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-5.3-Codex uniquely exposes Vision, Reasoning, and Function calling in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
GLM-4 Code 9B
Unavailable
No complete token price in local provider data
GPT-5.3-Codex
$4,900
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 GLM-4 Code 9B and GPT-5.3-Codex; plan for SDK, billing, or endpoint changes.
- GPT-5.3-Codex adds Vision, Reasoning, and Function calling in local capability data.
- No overlapping tracked provider route is sourced for GPT-5.3-Codex and GLM-4 Code 9B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Reasoning, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-05-10 | 2026-02-05 |
| Context window | — | 400K |
| Parameters | 9B | — |
| Architecture | - | decoder only |
| License | Apache 2.0 | Proprietary |
| Knowledge cutoff | - | 2025-08 |
Pricing and availability
| Pricing attribute | GLM-4 Code 9B | GPT-5.3-Codex |
|---|---|---|
| Input price | - | $1.75/1M tokens |
| Output price | - | $14/1M tokens |
| Providers | - |
Capabilities
| Capability | GLM-4 Code 9B | GPT-5.3-Codex |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | Yes |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GPT-5.3-Codex, reasoning mode: GPT-5.3-Codex, function calling: GPT-5.3-Codex, tool use: GPT-5.3-Codex, structured outputs: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. 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: GLM-4 Code 9B has no token price sourced yet and GPT-5.3-Codex has $1.75/1M 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 GLM-4 Code 9B when provider fit are central to the workload. Choose GPT-5.3-Codex when coding workflow support 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 GLM-4 Code 9B or GPT-5.3-Codex open source?
GLM-4 Code 9B is listed under Apache 2.0. GPT-5.3-Codex 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 vision, GLM-4 Code 9B or GPT-5.3-Codex?
GPT-5.3-Codex has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for reasoning mode, GLM-4 Code 9B or GPT-5.3-Codex?
GPT-5.3-Codex 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, GLM-4 Code 9B or GPT-5.3-Codex?
GPT-5.3-Codex 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, GLM-4 Code 9B or GPT-5.3-Codex?
GPT-5.3-Codex 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.
Where can I run GLM-4 Code 9B and GPT-5.3-Codex?
GLM-4 Code 9B is available on the tracked providers still being sourced. GPT-5.3-Codex is available on OpenRouter and OpenAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.