Qwen3.7 Max
Qwen · released May 21, 2026
Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks,...
Specification
- Context window
- 1M
- Max output
- 131K
- Knowledge cutoff
- Not stated
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 1
- Moderated
- No
- Uptime
- 100.0%
Intelligence
46.7
93th percentile
Coding
66.0
Coding Index
Agentic
30.9
Agentic Index
Output speed
47 t/s
Median across providers
Latency
1.23s
Time to first token
Cost per task
$0.43
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- τ²-bench (Telecom)94.7%
- GPQA Diamond92.3%
- IFBench80.5%
- AA-LCR (long context)74.7%
- Terminal-Bench Hard50.8%
- SciCode48.8%
- Humanity's Last Exam40.5%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| τ²-bench (Telecom) | 94.7% |
| GPQA Diamond | 92.3% |
| IFBench | 80.5% |
| AA-LCR (long context) | 74.7% |
| Terminal-Bench Hard | 50.8% |
| SciCode | 48.8% |
| Humanity's Last Exam | 40.5% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Gemini 3.1 Pro PreviewGoogle47.7
- Qwen3.7 MaxQwen46.7
- GPT-5.3-CodexOpenAI45.5
- MiniMax M3MiniMax45.4
- V4 ProDeepSeek45.3
- Kimi K2.6Moonshot AI45.1
- GPT-5.2OpenAI43.3
- Kimi K2.7 CodeMoonshot AI43.0
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Gemini 3.1 Pro Preview | 47.7 |
| Qwen3.7 Max | 46.7 |
| GPT-5.3-Codex | 45.5 |
| MiniMax M3 | 45.4 |
| V4 Pro | 45.3 |
| Kimi K2.6 | 45.1 |
| GPT-5.2 | 43.3 |
| Kimi K2.7 Code | 43.0 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $1.48
- Output / 1M tokens
- $4.42
- Cached input / 1M
- $0.295
- Blended 3:1
- $2.21
One task, estimated
$0.43
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.
Arena
Head-to-head generation quality
Elo from pairwise judgements, broken out by the kind of thing the model was asked to build.
- Overall Elo
- 1,322
- Win rate
- 56.8%
- Strongest at
- 3D scenes
- Tournaments
- 4,167
Elo by category
Dot position on a 1,200–1,325 scale · Elo has no meaningful zero
- 1,322
- 1,318
- 1,311
- 1,309
- 1,296
- 1,265
- 1,240
- 1,206
Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.
View as table
| Category | Elo | Win rate |
|---|---|---|
| 3D scenes | 1322 | 56.8% |
| Data visualisation | 1318 | 58.1% |
| Game development | 1311 | 57.6% |
| UI components | 1309 | 55.5% |
| Websites | 1296 | 56.5% |
| SVG | 1265 | 59.4% |
| Full-stack apps | 1240 | 55.6% |
| Mobile apps | 1206 | 50.4% |