GLM-5.1 vs o3 Mini
GLM-5.1 (2026) and o3 Mini (2025) are frontier-tier reasoning models from Zhipu AI and OpenAI. GLM-5.1 ships a 200k-token context window, while o3 Mini ships a not-yet-sourced context window. On Google-Proof Q&A, GLM-5.1 leads by 7.1 pts. On pricing, GLM-5.1 costs $0.95/1M input tokens versus $1.1/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Pick GLM-5.1 for reasoning; o3 Mini is better when coding workflow support matters more.
Specs
Pricing and availability
| GLM-5.1 | o3 Mini | |
|---|---|---|
| Input price | $0.95/1M tokens | $1.1/1M tokens |
| Output price | $3.15/1M tokens | $4.4/1M tokens |
| Providers |
Capabilities
| GLM-5.1 | o3 Mini | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | GLM-5.1 | o3 Mini |
|---|---|---|
| Google-Proof Q&A | 86.8 | 79.7 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has GLM-5.1 at 86.8 and o3 Mini at 79.7, with GLM-5.1 ahead by 7.1 points. The largest visible gap is 7.1 points on Google-Proof Q&A, 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 is close: both models cover reasoning mode, function calling, tool use, structured outputs, and code execution. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, GLM-5.1 lists $0.95/1M input and $3.15/1M output tokens, while o3 Mini lists $1.1/1M input and $4.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GLM-5.1 lower by about $0.48 per million blended tokens. Availability is 2 providers versus 3, so concentration risk also matters.
Choose GLM-5.1 when coding workflow support and lower input-token cost are central to the workload. Choose o3 Mini 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.
FAQ
Which is cheaper, GLM-5.1 or o3 Mini?
GLM-5.1 is cheaper on tracked token pricing. GLM-5.1 costs $0.95/1M input and $3.15/1M output tokens. o3 Mini costs $1.1/1M input and $4.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5.1 or o3 Mini open source?
GLM-5.1 is listed under Proprietary. o3 Mini 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 reasoning mode, GLM-5.1 or o3 Mini?
Both GLM-5.1 and o3 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.
Which is better for function calling, GLM-5.1 or o3 Mini?
Both GLM-5.1 and o3 Mini expose function calling. 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.
Which is better for tool use, GLM-5.1 or o3 Mini?
Both GLM-5.1 and o3 Mini expose tool use. 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-5.1 and o3 Mini?
GLM-5.1 is available on Z.ai and OpenRouter. o3 Mini is available on OpenRouter, OpenAI Batch API, and Azure OpenAI. 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.