GLM-4 32B vs GPT-5.3-Codex
GLM-4 32B (2025) and GPT-5.3-Codex (2026) are agentic coding models from Tsinghua Knowledge Engineering Group (THUDM) and OpenAI. GLM-4 32B ships a not-yet-sourced context window, while GPT-5.3-Codex ships a 400K-token context window. On pricing, GLM-4 32B costs $0.1/1M input tokens versus $1.75/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
GLM-4 32B is ~1650% cheaper at $0.1/1M; pay for GPT-5.3-Codex only for coding workflow support.
Decision scorecard
Local evidence first| Signal | GLM-4 32B | GPT-5.3-Codex |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | Coding, RAG, and Agents |
| Context window | — | 400K |
| Cheapest output | $0.1/1M tokens | $14/1M tokens |
| Provider routes | 1 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-4 32B has the lower cheapest tracked output price at $0.1/1M tokens.
- Local decision data tags GLM-4 32B for Classification and JSON / Tool use.
- 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 32B
$105
Cheapest tracked route: OpenRouter
GPT-5.3-Codex
$4,900
Cheapest tracked route: OpenRouter
Estimated monthly gap: $4,795. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- GPT-5.3-Codex is $13.90/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-5.3-Codex adds Vision, Reasoning, and Function calling in local capability data.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- GLM-4 32B is $13.90/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Reasoning, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-05 | 2026-02-05 |
| Context window | — | 400K |
| Parameters | 32B | — |
| Architecture | - | decoder only |
| License | Apache 2.0 | Proprietary |
| Knowledge cutoff | - | 2025-08 |
Pricing and availability
| Pricing attribute | GLM-4 32B | GPT-5.3-Codex |
|---|---|---|
| Input price | $0.1/1M tokens | $1.75/1M tokens |
| Output price | $0.1/1M tokens | $14/1M tokens |
| Providers |
Capabilities
| Capability | GLM-4 32B | GPT-5.3-Codex |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | 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, and code execution: GPT-5.3-Codex. Both models share structured outputs, 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.
For cost, GLM-4 32B lists $0.1/1M input and $0.1/1M output tokens, while GPT-5.3-Codex lists $1.75/1M input and $14/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GLM-4 32B lower by about $5.33 per million blended tokens. Availability is 1 providers versus 2, so concentration risk also matters.
Choose GLM-4 32B when provider fit and lower input-token cost 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.
FAQ
Which is cheaper, GLM-4 32B or GPT-5.3-Codex?
GLM-4 32B is cheaper on tracked token pricing. GLM-4 32B costs $0.1/1M input and $0.1/1M output tokens. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-4 32B or GPT-5.3-Codex open source?
GLM-4 32B 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 32B 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 32B 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 32B 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.
Where can I run GLM-4 32B and GPT-5.3-Codex?
GLM-4 32B is available on OpenRouter. GPT-5.3-Codex is available on OpenRouter and OpenAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.