GLM-5 vs GPT-5.2 Codex
GLM-5 and GPT-5.2 Codex solve the same coding-agent problem from opposite deployment models. GLM-5 is Zhipu AI's open-source (MIT) 200K-context foundation model with a tracked SWE-bench Verified row and multiple provider routes. GPT-5.2 Codex is OpenAI's proprietary coding model for long-horizon agentic engineering, with a 400K-token API context window and official OpenAI pricing. Use this comparison to decide whether openness and routability matter more than OpenAI's managed Codex model path.
Pick GLM-5 when you need open-source (MIT), downloadable weights, self-hosting options, or provider portability, especially for teams that want to inspect or run the model outside a closed API. Pick GPT-5.2 Codex when you want OpenAI's API-hosted coding model with a larger 400K context window, image input, 128K max output, and official $1.75/M input and $14/M output pricing. Do not mark GPT-5.2 Codex unavailable just because older Codex sign-in model choices were deprecated; the API model page remains the source of truth for API availability.
Decision scorecard
Local evidence first| Signal | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| Product type | Standalone API model | Coding-specialized model |
| Best for | reasoning-heavy apps, tool-calling agents, and provider-routed production | custom coding agents, code generation, and tool loops |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 200k | 400k |
| Cheapest output | $2.08/1M tokens | $14/1M tokens |
| Provider routes | 7 tracked | 2 tracked |
| Shared benchmarks | SWE-bench Verified leader | 2 shared |
Decision tradeoffs
- GLM-5 holds a shared-benchmark lead on SWE-bench Verified, ahead by 5 points.
- GLM-5 has the lower cheapest tracked output price at $2.08/1M tokens.
- GLM-5 has broader tracked provider coverage for fallback and route flexibility.
- Local decision data tags GLM-5 for Coding, RAG, and Agents.
- GPT-5.2 Codex holds a shared-benchmark lead on SWE-bench Pro, ahead by 1.3 points.
- GPT-5.2 Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.2 Codex uniquely exposes Vision, Multimodal, and Code execution in local model data.
- Local decision data tags GPT-5.2 Codex for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5
$1,000
Cheapest tracked route/tier: OpenRouter
GPT-5.2 Codex
$4,900
Cheapest tracked route/tier: Vercel AI Gateway
Estimated monthly gap: $3,900. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.2 Codex is $11.92/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-5.2 Codex adds Vision, Multimodal, and Code execution in local capability data.
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- GLM-5 is $11.92/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Code execution before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-11 | 2025-12-18 |
| Context window | 200k | 400k |
| Parameters | 744B total, 40B active | — |
| Architecture | Mixture of Experts | Decoder Only |
| License | MITOSI-approved | Proprietary |
| Openness | Open source | Proprietary |
| Weights | Available | Not released |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| Knowledge cutoff | 2025-11 | 2025-08 |
Pricing and availability
| Pricing attribute | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| Input price | $0.60/1M tokens | $1.75/1M tokens |
| Output price | $2.08/1M tokens | $14/1M tokens |
| Providers |
Capabilities
| Capability | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| JSON / Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| SWE-bench Verified | 77.8 | 72.8 |
| SWE-bench Pro | 55.1 | 56.4 |
Continue comparing
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Last reviewed: 2026-06-30. Data sourced from public model cards and provider documentation.