GLM-5.1 vs Qwen2.5-72B-Instruct
GLM-5.1 (2026) and Qwen2.5-72B-Instruct (2024) are frontier reasoning models from Zhipu AI and Alibaba. GLM-5.1 ships a 200k-token context window, while Qwen2.5-72B-Instruct ships a 128K-token context window. On Google-Proof Q&A, GLM-5.1 leads by 21.4 pts. On pricing, Qwen2.5-72B-Instruct costs $0.12/1M input tokens versus $0.95/1M 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.
Qwen2.5-72B-Instruct is ~692% cheaper at $0.12/1M; pay for GLM-5.1 only for coding workflow support.
Specs
| Released | 2026-03-27 | 2024-06-07 |
| Context window | 200k | 128K |
| Parameters | 744B total, 40-44B active | 72.7B |
| Architecture | mixture of experts | decoder only |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| GLM-5.1 | Qwen2.5-72B-Instruct | |
|---|---|---|
| Input price | $0.95/1M tokens | $0.12/1M tokens |
| Output price | $3.15/1M tokens | $0.39/1M tokens |
| Providers |
Capabilities
| GLM-5.1 | Qwen2.5-72B-Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | GLM-5.1 | Qwen2.5-72B-Instruct |
|---|---|---|
| Google-Proof Q&A | 86.8 | 65.4 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has GLM-5.1 at 86.8 and Qwen2.5-72B-Instruct at 65.4, with GLM-5.1 ahead by 21.4 points. The largest visible gap is 21.4 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 differs most on reasoning mode: GLM-5.1, function calling: GLM-5.1, tool use: GLM-5.1, and code execution: GLM-5.1. 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-5.1 lists $0.95/1M input and $3.15/1M output tokens, while Qwen2.5-72B-Instruct lists $0.12/1M input and $0.39/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2.5-72B-Instruct lower by about $1.41 per million blended tokens. Availability is 2 providers versus 7, so concentration risk also matters.
Choose GLM-5.1 when coding workflow support and larger context windows are central to the workload. Choose Qwen2.5-72B-Instruct when provider fit, lower input-token cost, 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 has a larger context window, GLM-5.1 or Qwen2.5-72B-Instruct?
GLM-5.1 supports 200k tokens, while Qwen2.5-72B-Instruct supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, GLM-5.1 or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is cheaper on tracked token pricing. GLM-5.1 costs $0.95/1M input and $3.15/1M output tokens. Qwen2.5-72B-Instruct costs $0.12/1M input and $0.39/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5.1 or Qwen2.5-72B-Instruct open source?
GLM-5.1 is listed under Proprietary. Qwen2.5-72B-Instruct is listed under Apache 2.0. 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 Qwen2.5-72B-Instruct?
GLM-5.1 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-5.1 or Qwen2.5-72B-Instruct?
GLM-5.1 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-5.1 and Qwen2.5-72B-Instruct?
GLM-5.1 is available on Z.ai and OpenRouter. Qwen2.5-72B-Instruct is available on DeepInfra, OpenRouter, Fireworks AI, Novita AI, and Chutes AI. 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.