GLM-4.6 Instruct vs Qwen3: which model should you choose?
We compared GLM-4.6 Instruct and Qwen3 on price, context window and 12 benchmarks. GLM-4.6 Instruct has the stronger average benchmark score; Qwen3 is cheaper on a blended 3:1 basis.
GLM-4.6 Instruct
GLM-4.6 Instruct is a multimodal model from Zhipu AI. It offers a 1,000,000-token context window, costs $5.15 per million input tokens and $24.32 per million output tokens, and averages 80.4 across the index's benchmark suite.
- Input
- $5.15 / 1M
- Output
- $24.32 / 1M
- Context
- 1,000,000
- Avg score
- 80.4
- 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 Instruct | Qwen3 | Edge |
|---|---|---|---|
| Input price / 1M | $5.15 | $5.04 | B |
| Output price / 1M | $24.32 | $22.52 | B |
| Context window | 1,000,000 | 128,000 | A |
| Max output | 128,000 | 8,192 | A |
| Average score | 80.4 | 80.3 | A |
| Arena Elo | 1426.6 | 1420.9 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 Instruct wins 5, Qwen3 wins 6, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 Instruct | Qwen3 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 80.6 | 81.2 | 0.6 | Qwen3 |
| GPQA Diamond | 75.8 | 77.7 | 1.9 | Qwen3 |
| SWE-bench Verified | 70.5 | 71.6 | 1.1 | Qwen3 |
| HumanEval | 85.3 | 87.0 | 1.7 | Qwen3 |
| MATH-500 | 84.9 | 86.5 | 1.6 | Qwen3 |
| AIME 2025 | 74.5 | 74.5 | 0.0 | Tie |
| LiveCodeBench | 72.6 | 73.8 | 1.2 | Qwen3 |
| IFEval | 87.4 | 85.6 | 1.8 | GLM-4.6 Instruct |
| TruthfulQA | 83.8 | 80.4 | 3.4 | GLM-4.6 Instruct |
| HellaSwag | 90.6 | 88.1 | 2.5 | GLM-4.6 Instruct |
| MMMU | 78.9 | 76.7 | 2.2 | GLM-4.6 Instruct |
| Arena Elo | 1426.6 | 1420.9 | 5.7 | GLM-4.6 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, GLM-4.6 Instruct costs about $9.94 per million tokens and Qwen3 costs about $9.41. Qwen3 is roughly 5% cheaper than GLM-4.6 Instruct. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GLM-4.6 Instruct has the larger context window at 1,000,000 tokens, compared with 128,000 for Qwen3. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 Instruct wins 5, Qwen3 wins 6, and 1 are tied. On the index average, GLM-4.6 Instruct leads Qwen3 by 0.1 points (80.4 versus 80.3).
The price picture favours Qwen3. The context picture favours GLM-4.6 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 Instruct for quality-sensitive work and Qwen3 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose GLM-4.6 Instruct if
- You want the higher average benchmark score in this index.
- You need the larger 1,000,000-token context window.
- You need the larger 128,000-token output limit.
Choose Qwen3 if
- Unit cost matters and you want the cheaper blended price.
FAQ
Is GLM-4.6 Instruct better than Qwen3?
On the index average, GLM-4.6 Instruct leads Qwen3 by 0.1 points (80.4 versus 80.3). The better choice depends on price, context and your task. GLM-4.6 Instruct wins 5 benchmarks, Qwen3 wins 6.
Which is cheaper, GLM-4.6 Instruct or Qwen3?
Qwen3 is cheaper on a blended 3:1 basis at about $9.41 per million tokens, versus $9.94 for GLM-4.6 Instruct.
Which has the larger context window?
GLM-4.6 Instruct has the larger context window at 1,000,000 tokens. Qwen3 has 128,000 tokens.
Are these models open weights?
GLM-4.6 Instruct is open weights; Qwen3 is open weights.
Which should I choose for production?
If you need a single default, start with GLM-4.6 Instruct for quality-sensitive work and Qwen3 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.