AI model comparison
Qwen3.6 27B vs GLM 4.7
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Qwen
Qwen3.6 27B
Qwen3.6 27B is a dense 27-billion-parameter language model from the Qwen Team at Alibaba, released in April 2026.
- Input / 1M
- $0.32
- Output / 1M
- $3.20
- Context
- 262K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Qwen3.6 27B
1.0x cheaper for a typical chat
Higher intelligence score
GLM 4.7
22.2 vs 21.4 on Artificial Analysis
Larger context window
Qwen3.6 27B
262K vs 205K tokens
Newer release
Qwen3.6 27B
Released April 27, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Qwen3.6 27B21.4Coding index
Qwen3.6 27B53.7Agentic index
Qwen3.6 27B18.5Pricing
Qwen3.6 27B vs GLM 4.7 API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Qwen3.6 27B | |
|---|---|---|
| Input tokensPer 1M tokens | $0.32 | $0.60 |
| Output tokensPer 1M tokens | $3.20 | $2.20 |
| Cached input (read)Per 1M tokens | Not available | $0.11 |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Qwen3.6 27B$2.24Coding task
30K in, 4K out
Qwen3.6 27B$22.40Long document summary
150K in, 2K out
Qwen3.6 27B$54.40Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Qwen3.6 27B | |
|---|---|---|
| Context window | 262K tokens | 205K tokens |
| Max output | 82K tokens | 131K tokens |
| Input types | Image, Text, Video | Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | April 27, 2026 | December 22, 2025 |
Features
Supported features
API features available for each model.
| Metric | Qwen3.6 27B | |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
- GLM 4.7 vs DeepSeek V4 Flash 0423
- GLM 4.7 vs GLM 5
- GLM 4.7 vs GLM 5.1
- GLM 4.7 vs Gemini 3.5 Flash Lite
- GLM 4.7 vs Kimi K2.5
- GLM 4.7 vs GLM 5V Turbo
- GLM 4.7 vs Nemotron 3 Ultra
- GLM 4.7 vs Qwen3.5-27B
- GLM 4.7 vs Kimi K2 Thinking
- GLM 4.7 vs Qwen3.5 397B A17B
- GLM 4.7 vs GPT-5 Mini
- GLM 4.7 vs GPT-5.1-Codex-Mini
- GLM 4.7 vs GLM 4.6
Our take
Qwen3.6 27B is 1.0x cheaper for a typical chat. GLM 4.7 scores higher on the Artificial Analysis Intelligence Index (22.2 vs 21.4). Qwen3.6 27B has the larger context window (262K vs 205K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Qwen3.6 27B or GLM 4.7 cheaper?
Qwen3.6 27B is cheaper for a typical chat (2K input tokens and 500 output tokens). Qwen3.6 27B costs $0.32 per 1M input tokens and $3.20 per 1M output tokens, while GLM 4.7 costs $0.60 per 1M input tokens and $2.20 per 1M output tokens.
Which has a bigger context window, Qwen3.6 27B or GLM 4.7?
Qwen3.6 27B supports up to 262K tokens of context, compared with 205K for GLM 4.7.
Is Qwen3.6 27B smarter than GLM 4.7?
On the Artificial Analysis Intelligence Index, Qwen3.6 27B scores 21.4 and GLM 4.7 scores 22.2, putting GLM 4.7 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Qwen3.6 27B and GLM 4.7 at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Qwen3.6 27B and GLM 4.7 answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Qwen3.6 27B and GLM 4.7 at the same time. Compare the answers side by side and keep the best one.