Model comparison
GLM-5.1 vs Qwen3.6
- Input
- $1.40 / 1M tokens
- Output
- $4.40 / 1M tokens
- Context
- 200K
- Released
- 2026-04-07
- Input
- $0.2885 / 1M tokens
- Output
- $3.17 / 1M tokens
- Context
- 262K (extensible ~1M)
- Released
- 2026-04
GLM-5.1 scores higher on 2 of the 3 benchmarks both models report (the other leads on 1); Qwen3.6 is cheaper on input tokens ($0.3 vs $1.4 per 1M, 4.9×); Qwen3.6 has the larger context window (262K vs 200K).
Specs and pricing side by side
| Spec | GLM-5.1 | Qwen3.6 |
|---|---|---|
| Maker | Z.ai (Zhipu / GLM) | Alibaba (Qwen) |
| Released | 2026-04-07 | 2026-04 |
| Context window | 200K | 262K (extensible ~1M) |
| Max output | 128K | ~32K (up to ~82K for complex reasoning) |
| Input price | $1.40 / 1M tokens | $0.2885 / 1M tokens |
| Output price | $4.40 / 1M tokens | $3.17 / 1M tokens |
| Parameters | ~754B total, 40B active (MoE) | 27B dense (Qwen3.6-27B); 35B total / 3B active MoE (Qwen3.6-35B-A3B) |
| License | MIT | Apache 2.0 |
| Weights | Open weights | Open weightssame |
| Modalities | Text | Text, Vision, Video |
| Knowledge cutoff | not published | Not publicly disclosed |
Benchmarks both models report
| Benchmark | GLM-5.1 | Qwen3.6 |
|---|---|---|
| AIME 2026 | 95.3% | 94.1% |
| GPQA-Diamond | 86.2% | 87.8% |
| Terminal-Bench 2.0 | 63.5% | 59.3% |
Scores are as published by each maker or its cited source; harness, reasoning effort and date can differ between rows, so treat small gaps as ties. Sources are listed on each model page.
Which one should you pick?
Pick GLM-5.1 if…
- You care about AIME 2026, Terminal-Bench 2.0 (it scores higher on 2 shared benchmarks).
- Your use case is: long-running autonomous coding agents that plan, execute and self-correct over many iterations.
Pick Qwen3.6 if…
- You care about GPQA-Diamond (it scores higher on 1 shared benchmark).
- Cost matters: input tokens cost 4.9× less ($0.3 vs $1.4 per 1M).
- You need the longer context (262K vs 200K).
- You need Vision / Video input, which GLM-5.1 lacks.
- Your use case is: agentic coding and repository-level software engineering (SWE-bench / Terminal-Bench style tasks).
FAQ
Which is better, GLM-5.1 or Qwen3.6?
GLM-5.1 scores higher on 2 of the 3 benchmarks both models report (the other leads on 1); Qwen3.6 is cheaper on input tokens ($0.3 vs $1.4 per 1M, 4.9×); Qwen3.6 has the larger context window (262K vs 200K). The better pick depends on whether you value benchmark scores, price per token, context length or openness for your workload.
How much cheaper is Qwen3.6?
GLM-5.1: $1.40 / 1M tokens input, $4.40 / 1M tokens output. Qwen3.6: $0.2885 / 1M tokens input, $3.17 / 1M tokens output (list API prices as published by each maker).
What are the context windows of GLM-5.1 and Qwen3.6?
GLM-5.1 offers 200K with up to 128K output; Qwen3.6 offers 262K (extensible ~1M) with up to ~32K (up to ~82K for complex reasoning) output.
Is GLM-5.1 newer than Qwen3.6?
GLM-5.1 was released 2026-04-07; Qwen3.6 was released 2026-04.