GLM-4.6 vs GLM-4.6 Instruct: which model should you choose?
We compared GLM-4.6 and GLM-4.6 Instruct on price, context window and 12 benchmarks. GLM-4.6 Instruct 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
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
Specification comparison
| Spec | GLM-4.6 | GLM-4.6 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.10 | $5.15 | A |
| Output price / 1M | $18.45 | $24.32 | A |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 8,192 | 128,000 | B |
| Average score | 80.2 | 80.4 | B |
| Arena Elo | 1440.7 | 1426.6 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 wins 4, GLM-4.6 Instruct wins 8, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 | GLM-4.6 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 80.6 | 3.4 | GLM-4.6 |
| GPQA Diamond | 78.3 | 75.8 | 2.5 | GLM-4.6 |
| SWE-bench Verified | 67.4 | 70.5 | 3.1 | GLM-4.6 Instruct |
| HumanEval | 83.8 | 85.3 | 1.5 | GLM-4.6 Instruct |
| MATH-500 | 83.4 | 84.9 | 1.5 | GLM-4.6 Instruct |
| AIME 2025 | 80.2 | 74.5 | 5.7 | GLM-4.6 |
| LiveCodeBench | 71.6 | 72.6 | 1.0 | GLM-4.6 Instruct |
| IFEval | 86.4 | 87.4 | 1.0 | GLM-4.6 Instruct |
| TruthfulQA | 82.5 | 83.8 | 1.3 | GLM-4.6 Instruct |
| HellaSwag | 89.4 | 90.6 | 1.2 | GLM-4.6 Instruct |
| MMMU | 75.4 | 78.9 | 3.5 | GLM-4.6 Instruct |
| Arena Elo | 1440.7 | 1426.6 | 14.1 | 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 GLM-4.6 Instruct costs about $9.94. GLM-4.6 is roughly 15% 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 GLM-4.6. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 wins 4, GLM-4.6 Instruct wins 8, and 0 are tied. On the index average, GLM-4.6 Instruct leads GLM-4.6 by 0.2 points (80.4 versus 80.2).
The price picture favours GLM-4.6. 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 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 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.
FAQ
Is GLM-4.6 better than GLM-4.6 Instruct?
On the index average, GLM-4.6 Instruct leads GLM-4.6 by 0.2 points (80.4 versus 80.2). The better choice depends on price, context and your task. GLM-4.6 wins 4 benchmarks, GLM-4.6 Instruct wins 8.
Which is cheaper, GLM-4.6 or GLM-4.6 Instruct?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 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. GLM-4.6 has 128,000 tokens.
Are these models open weights?
GLM-4.6 is open weights; GLM-4.6 Instruct 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 GLM-4.6 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.