GLM-4.6 7B: pricing, context window and benchmarks
GLM-4.6 7B is a small open-weight model from Zhipu AI. It offers a 16,384-token context window, costs $1.52 per million input tokens and $6.98 per million output tokens, and averages 61.8 across the index's benchmark suite.
Pricing
GLM-4.6 7B costs $1.52 per million input tokens and $6.98 per million output tokens. A blended 3:1 input-to-output mix works out to about $2.89 per million tokens. Cached input is listed at $0.15 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $1.52 | per 1M tokens |
| Output | $6.98 | per 1M tokens |
| Cached input | $0.15 | per 1M tokens |
| Blended 3:1 | $2.89 | estimate per 1M tokens |
| 1M in + 1M out | $8.50 | 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 | 65.6 | 443 | 26th | |
| GPQA Diamond | 58.0 | 427 | 29th | |
| SWE-bench Verified | 49.5 | 425 | 29th | |
| HumanEval | 66.6 | 424 | 29th | |
| MATH-500 | 65.4 | 416 | 31th | |
| AIME 2025 | 55.7 | 412 | 31th | |
| LiveCodeBench | 47.2 | 473 | 21th | |
| IFEval | 73.1 | 415 | 31th | |
| TruthfulQA | 65.6 | 451 | 25th | |
| HellaSwag | 75.4 | 434 | 28th | |
| MMMU | 58.2 | 456 | 24th | |
| Arena Elo | 1276.4 | 451 | 25th |
What GLM-4.6 7B is good for
GLM-4.6 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $1.52 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $1.52 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
Trade-offs
- Context window of 16,384 tokens is small for long-document work.
- Average benchmark score is below the index median; test it on your own task.
- Less reliable on open-ended reasoning and complex multi-step instructions.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Llama 4 Maverick 7B | Meta AI | 4096 | $1.52 | 61.9 |
| Sonar Nano | Perplexity | 4096 | $4.37 | 61.9 |
| StableLM 2 Base | Stability AI | 8192 | $1.49 | 61.9 |
| GLM-4.6 7B | Zhipu AI | 16384 | $1.52 | 61.8 |
| GPT-NeoX | EleutherAI | 16384 | $1.57 | 61.7 |
| MiniMax-M2 8B | MiniMax | 32768 | $1.65 | 61.7 |
FAQ
How much does GLM-4.6 7B cost?
GLM-4.6 7B is listed at $1.52 per million input tokens and $6.98 per million output tokens. Cached input is $0.15 per million tokens. A blended 3:1 input-to-output mix is about $2.89 per million tokens.
What is the context window of GLM-4.6 7B?
GLM-4.6 7B has a context window of 16,384 tokens and can emit up to 4,096 output tokens per request in this index.
Is GLM-4.6 7B open weights?
Yes. GLM-4.6 7B is listed as an open-weight model under a open weights licence.
How does GLM-4.6 7B score on benchmarks?
GLM-4.6 7B averages 61.8 across the scored benchmarks in this index, ranking #433 of 600 models. Its strongest relative result is Arena Elo at 1276.4.
What is GLM-4.6 7B best for?
GLM-4.6 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $1.52 per million input tokens it can handle a large share of production traffic.