GLM-4.6 vs Qwen3 Instruct: which model should you choose?
We compared GLM-4.6 and Qwen3 Instruct on price, context window and 12 benchmarks. GLM-4.6 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 Instruct
Qwen3 Instruct is a multimodal model from Alibaba Cloud. It offers a 1,000,000-token context window, costs $5.06 per million input tokens and $25.39 per million output tokens, and averages 79.8 across the index's benchmark suite.
- Input
- $5.06 / 1M
- Output
- $25.39 / 1M
- Context
- 1,000,000
- Avg score
- 79.8
- Open weights
- Yes
Specification comparison
| Spec | GLM-4.6 | Qwen3 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.10 | $5.06 | B |
| Output price / 1M | $18.45 | $25.39 | A |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 8,192 | 128,000 | B |
| Average score | 80.2 | 79.8 | A |
| Arena Elo | 1440.7 | 1419.2 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 wins 7, Qwen3 Instruct wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 | Qwen3 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 80.3 | 3.7 | GLM-4.6 |
| GPQA Diamond | 78.3 | 74.1 | 4.2 | GLM-4.6 |
| SWE-bench Verified | 67.4 | 68.5 | 1.1 | Qwen3 Instruct |
| HumanEval | 83.8 | 85.1 | 1.3 | Qwen3 Instruct |
| MATH-500 | 83.4 | 86.1 | 2.7 | Qwen3 Instruct |
| AIME 2025 | 80.2 | 78.5 | 1.7 | GLM-4.6 |
| LiveCodeBench | 71.6 | 71.1 | 0.5 | GLM-4.6 |
| IFEval | 86.4 | 89.1 | 2.7 | Qwen3 Instruct |
| TruthfulQA | 82.5 | 79.7 | 2.8 | GLM-4.6 |
| HellaSwag | 89.4 | 87.0 | 2.4 | GLM-4.6 |
| MMMU | 75.4 | 78.7 | 3.3 | Qwen3 Instruct |
| Arena Elo | 1440.7 | 1419.2 | 21.5 | 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 Instruct costs about $10.14. GLM-4.6 is roughly 17% cheaper than Qwen3 Instruct. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Qwen3 Instruct has the larger context window at 1,000,000 tokens, compared with 128,000 for GLM-4.6. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 wins 7, Qwen3 Instruct wins 5, and 0 are tied. On the index average, GLM-4.6 leads Qwen3 Instruct by 0.4 points (80.2 versus 79.8).
The price picture favours GLM-4.6. The context picture favours Qwen3 Instruct. 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 GLM-4.6 for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose GLM-4.6 if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
Choose Qwen3 Instruct if
- You need the larger 1,000,000-token context window.
- You need the larger 128,000-token output limit.
FAQ
Is GLM-4.6 better than Qwen3 Instruct?
On the index average, GLM-4.6 leads Qwen3 Instruct by 0.4 points (80.2 versus 79.8). The better choice depends on price, context and your task. GLM-4.6 wins 7 benchmarks, Qwen3 Instruct wins 5.
Which is cheaper, GLM-4.6 or Qwen3 Instruct?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 per million tokens, versus $10.14 for Qwen3 Instruct.
Which has the larger context window?
Qwen3 Instruct has the larger context window at 1,000,000 tokens. GLM-4.6 has 128,000 tokens.
Are these models open weights?
GLM-4.6 is open weights; Qwen3 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with GLM-4.6 for quality-sensitive work, and check whether its price holds at your expected volume before committing.