GLM-5 vs o4-mini
GLM-5 (2026) and o4-mini (2025) are frontier-tier reasoning models from Zhipu AI and OpenAI. GLM-5 ships a 200k-token context window, while o4-mini ships a not-yet-sourced context window. On SWE-bench Verified, GLM-5 leads by 9.7 pts. On pricing, o4-mini costs $0.5/1M input tokens versus $0.72/1M 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.
o4-mini is ~44% cheaper at $0.5/1M; pay for GLM-5 only for provider fit.
Specs
Pricing and availability
| GLM-5 | o4-mini | |
|---|---|---|
| Input price | $0.72/1M tokens | $0.5/1M tokens |
| Output price | $2.3/1M tokens | $2/1M tokens |
| Providers |
Capabilities
| GLM-5 | o4-mini | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | GLM-5 | o4-mini |
|---|---|---|
| SWE-bench Verified | 77.8 | 68.1 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has GLM-5 at 77.8 and o4-mini at 68.1, with GLM-5 ahead by 9.7 points. The largest visible gap is 9.7 points on SWE-bench Verified, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: o4-mini, multimodal input: o4-mini, and code execution: o4-mini. Both models share reasoning mode, function calling, tool use, and 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-5 lists $0.72/1M input and $2.3/1M output tokens, while o4-mini lists $0.5/1M input and $2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts o4-mini lower by about $0.24 per million blended tokens. Availability is 5 providers versus 4, so concentration risk also matters.
Choose GLM-5 when provider fit and broader provider choice are central to the workload. Choose o4-mini when coding workflow support and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which is cheaper, GLM-5 or o4-mini?
o4-mini is cheaper on tracked token pricing. GLM-5 costs $0.72/1M input and $2.3/1M output tokens. o4-mini costs $0.5/1M input and $2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5 or o4-mini open source?
GLM-5 is listed under MIT. o4-mini 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-5 or o4-mini?
o4-mini 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 multimodal input, GLM-5 or o4-mini?
o4-mini has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for reasoning mode, GLM-5 or o4-mini?
Both GLM-5 and o4-mini expose reasoning mode. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run GLM-5 and o4-mini?
GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. o4-mini is available on OpenAI API, OpenRouter, OpenAI Batch API, and Replicate API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.