GLM-4.6 vs Llama 4 Maverick Chat: which model should you choose?
We compared GLM-4.6 and Llama 4 Maverick Chat 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
Llama 4 Maverick Chat
Llama 4 Maverick Chat is a multimodal model from Meta AI. It offers a 128,000-token context window, costs $5.09 per million input tokens and $23.20 per million output tokens, and averages 79.7 across the index's benchmark suite.
- Input
- $5.09 / 1M
- Output
- $23.20 / 1M
- Context
- 128,000
- Avg score
- 79.7
- Open weights
- Yes
Specification comparison
| Spec | GLM-4.6 | Llama 4 Maverick Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.10 | $5.09 | B |
| Output price / 1M | $18.45 | $23.20 | A |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 8,192 | 32,768 | B |
| Average score | 80.2 | 79.7 | A |
| Arena Elo | 1440.7 | 1419.9 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 wins 8, Llama 4 Maverick Chat wins 3, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 | Llama 4 Maverick Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 80.5 | 3.5 | GLM-4.6 |
| GPQA Diamond | 78.3 | 73.0 | 5.3 | GLM-4.6 |
| SWE-bench Verified | 67.4 | 66.6 | 0.8 | GLM-4.6 |
| HumanEval | 83.8 | 83.2 | 0.6 | GLM-4.6 |
| MATH-500 | 83.4 | 84.8 | 1.4 | Llama 4 Maverick Chat |
| AIME 2025 | 80.2 | 78.9 | 1.3 | GLM-4.6 |
| LiveCodeBench | 71.6 | 76.3 | 4.7 | Llama 4 Maverick Chat |
| IFEval | 86.4 | 87.4 | 1.0 | Llama 4 Maverick Chat |
| TruthfulQA | 82.5 | 81.5 | 1.0 | GLM-4.6 |
| HellaSwag | 89.4 | 88.8 | 0.6 | GLM-4.6 |
| MMMU | 75.4 | 75.4 | 0.0 | Tie |
| Arena Elo | 1440.7 | 1419.9 | 20.8 | 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 Llama 4 Maverick Chat costs about $9.62. GLM-4.6 is roughly 12% cheaper than Llama 4 Maverick Chat. 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 8, Llama 4 Maverick Chat wins 3, and 1 are tied. On the index average, GLM-4.6 leads Llama 4 Maverick Chat by 0.5 points (80.2 versus 79.7).
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 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 Llama 4 Maverick Chat if
- You need the larger 32,768-token output limit.
FAQ
Is GLM-4.6 better than Llama 4 Maverick Chat?
On the index average, GLM-4.6 leads Llama 4 Maverick Chat by 0.5 points (80.2 versus 79.7). The better choice depends on price, context and your task. GLM-4.6 wins 8 benchmarks, Llama 4 Maverick Chat wins 3.
Which is cheaper, GLM-4.6 or Llama 4 Maverick Chat?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 per million tokens, versus $9.62 for Llama 4 Maverick Chat.
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; Llama 4 Maverick Chat 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.