GLM-4.6 vs Qwen3: which model should you choose?
We compared GLM-4.6 and Qwen3 on price, context window and 12 benchmarks. Qwen3 has the stronger average benchmark score; GLM-4.6 is cheaper on a blended 3:1 basis.
GLM-4.6
GLM-4.6 is a multimodal model from Zhipu AI. It offers a 128,000-token context window, costs $5.10 per million input tokens and $18.45 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.10 / 1M
- Output
- $18.45 / 1M
- Context
- 128,000
- Avg score
- 80.2
- Open weights
- Yes
Qwen3
Qwen3 is a multimodal model from Alibaba Cloud. It offers a 128,000-token context window, costs $5.04 per million input tokens and $22.52 per million output tokens, and averages 80.3 across the index's benchmark suite.
- Input
- $5.04 / 1M
- Output
- $22.52 / 1M
- Context
- 128,000
- Avg score
- 80.3
- Open weights
- Yes
Specification comparison
| Spec | GLM-4.6 | Qwen3 | Edge |
|---|---|---|---|
| Input price / 1M | $5.10 | $5.04 | B |
| Output price / 1M | $18.45 | $22.52 | A |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 8,192 | 8,192 | Tie |
| Average score | 80.2 | 80.3 | B |
| Arena Elo | 1440.7 | 1420.9 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 wins 7, Qwen3 wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 | Qwen3 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 81.2 | 2.8 | GLM-4.6 |
| GPQA Diamond | 78.3 | 77.7 | 0.6 | GLM-4.6 |
| SWE-bench Verified | 67.4 | 71.6 | 4.2 | Qwen3 |
| HumanEval | 83.8 | 87.0 | 3.2 | Qwen3 |
| MATH-500 | 83.4 | 86.5 | 3.1 | Qwen3 |
| AIME 2025 | 80.2 | 74.5 | 5.7 | GLM-4.6 |
| LiveCodeBench | 71.6 | 73.8 | 2.2 | Qwen3 |
| IFEval | 86.4 | 85.6 | 0.8 | GLM-4.6 |
| TruthfulQA | 82.5 | 80.4 | 2.1 | GLM-4.6 |
| HellaSwag | 89.4 | 88.1 | 1.3 | GLM-4.6 |
| MMMU | 75.4 | 76.7 | 1.3 | Qwen3 |
| Arena Elo | 1440.7 | 1420.9 | 19.8 | GLM-4.6 |
Price analysis
On a blended 3:1 input-to-output basis, GLM-4.6 costs about $8.44 per million tokens and Qwen3 costs about $9.41. GLM-4.6 is roughly 10% cheaper than Qwen3. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 wins 7, Qwen3 wins 5, and 0 are tied. On the index average, Qwen3 leads GLM-4.6 by 0.1 points (80.3 versus 80.2).
The price picture favours GLM-4.6. The context picture is effectively a tie. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with Qwen3 for quality-sensitive work and GLM-4.6 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose GLM-4.6 if
- Unit cost matters and you want the cheaper blended price.
Choose Qwen3 if
- You want the higher average benchmark score in this index.
FAQ
Is GLM-4.6 better than Qwen3?
On the index average, Qwen3 leads GLM-4.6 by 0.1 points (80.3 versus 80.2). The better choice depends on price, context and your task. GLM-4.6 wins 7 benchmarks, Qwen3 wins 5.
Which is cheaper, GLM-4.6 or Qwen3?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 per million tokens, versus $9.41 for Qwen3.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
GLM-4.6 is open weights; Qwen3 is open weights.
Which should I choose for production?
If you need a single default, start with Qwen3 for quality-sensitive work and GLM-4.6 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.