GLM-4.6 Chat: pricing, context window and benchmarks
GLM-4.6 Chat is a multimodal model from Zhipu AI. It offers a 400,000-token context window, costs $4.89 per million input tokens and $21.46 per million output tokens, and averages 79.8 across the index's benchmark suite.
Pricing
GLM-4.6 Chat costs $4.89 per million input tokens and $21.46 per million output tokens. A blended 3:1 input-to-output mix works out to about $9.03 per million tokens. Cached input is listed at $0.62 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $4.89 | per 1M tokens |
| Output | $21.46 | per 1M tokens |
| Cached input | $0.62 | per 1M tokens |
| Blended 3:1 | $9.03 | estimate per 1M tokens |
| 1M in + 1M out | $26.35 | estimate |
Benchmarks
Scores below are modelled estimates on a 0–100 scale. Use them to compare relative position, not as official vendor results.
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 82.2 | 117 | 81th | |
| GPQA Diamond | 72.8 | 201 | 67th | |
| SWE-bench Verified | 66.0 | 193 | 68th | |
| HumanEval | 86.3 | 99 | 84th | |
| MATH-500 | 86.8 | 81 | 87th | |
| AIME 2025 | 79.2 | 99 | 84th | |
| LiveCodeBench | 69.6 | 200 | 67th | |
| IFEval | 87.8 | 108 | 82th | |
| TruthfulQA | 83.2 | 103 | 83th | |
| HellaSwag | 89.9 | 87 | 86th | |
| MMMU | 73.5 | 203 | 66th | |
| Arena Elo | 1437.7 | 103 | 83th |
What GLM-4.6 Chat is good for
GLM-4.6 Chat is tuned toward coding work. It is a reasonable candidate for code generation, review, debugging and repository-scale tasks, especially when paired with a retrieval layer. At $4.89 per million input tokens, it sits in the mid-range of the index, and the 400,000-token context window is enough for large files and multi-file diffs.
Strengths
- Large 400,000-token context window for long documents and agent traces.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
- Accepts more than text, including image input where the provider supports it.
- Coding benchmarks are a relative strength within its tier.
Trade-offs
- No standout weakness in the indexed specifications, but real-world behaviour still needs testing.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Command A Turbo | Cohere | 32768 | $13.53 | 79.8 |
| ERNIE 4.5 Reasoning | Baidu | 1000000 | $14.36 | 79.8 |
| GLM-4.6 Chat | Zhipu AI | 400000 | $4.89 | 79.8 |
| Magistral | Mistral AI | 200000 | $4.75 | 79.8 |
| Qwen3 Instruct | Alibaba Cloud | 1000000 | $5.06 | 79.8 |
| Doubao 1.5 Pro | ByteDance | 200000 | $13.65 | 79.7 |
FAQ
How much does GLM-4.6 Chat cost?
GLM-4.6 Chat is listed at $4.89 per million input tokens and $21.46 per million output tokens. Cached input is $0.62 per million tokens. A blended 3:1 input-to-output mix is about $9.03 per million tokens.
What is the context window of GLM-4.6 Chat?
GLM-4.6 Chat has a context window of 400,000 tokens and can emit up to 16,384 output tokens per request in this index.
Is GLM-4.6 Chat open weights?
Yes. GLM-4.6 Chat is listed as an open-weight model under a open weights licence.
How does GLM-4.6 Chat score on benchmarks?
GLM-4.6 Chat averages 79.8 across the scored benchmarks in this index, ranking #125 of 600 models. Its strongest relative result is Arena Elo at 1437.7.
What is GLM-4.6 Chat best for?
GLM-4.6 Chat is tuned toward coding work. It is a reasonable candidate for code generation, review, debugging and repository-scale tasks, especially when paired with a retrieval layer. At $4.89 per million input tokens, it sits in the mid-range of the index, and the 400,000-token context window is enough for large files and multi-file diffs.