Qwen3.7 Max: Price, Context, Benchmarks, and Release Details
58.6
SI Score (method si-v2-absolute-shrinkage-1)
#61 of 115 ranked
53% confidence 53 percent, Medium confidence — 3 of 7 expected sources in
Coverage 43% of expected source weight · 100% confidence at 80% coverage
Pillar breakdown
Coding (weight 40 percent) 60.1
Math (weight 15 percent) 73.5
Preference (weight 15 percent) —
Reasoning (weight 30 percent) 82.0
Pillar weights: reasoning 30% · math 15% · coding 40% · preference 15%. Benchmark results use fixed 0–100 scales before averaging; incomplete evidence is shrunk toward 50.
Facts
- Input price / 1M
- not yet reported
- Output price / 1M
- not yet reported
- Context window
- 1Mmodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - Max output
- 65.5Kmodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - Released
- May 21, 2026models.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - Open weights
- Nomodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - License
- not yet reported
- Input modalities
- textmodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗
Benchmark results
26 results| Benchmark | Raw result | Normalized (0–100) | Pillar |
|---|---|---|---|
| frontiermath tier 4 v2 | 34.1%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Jun 13, 2026
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 34.1 | math |
| frontiermath tiers 1 3 v2 | 64.6%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Jun 13, 2026
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 64.6 | math |
| gpqa diamond | 90.9%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Aug 7, 2026
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 90.9 | reasoning |
| gpqa diamond | 92.4%Official model cards via models.devLab-reported; metric accuracy; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published May 19, 2026
Retrieved Oct 8, 2026 · factual citation; MIT transcription Open source ↗ | 92.4 | reasoning |
| hle | 41.4%Official model cards via models.devLab-reported; metric accuracy; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published May 19, 2026
Retrieved Oct 8, 2026 · factual citation; MIT transcription Open source ↗ | 41.4 | reasoning |
| livebench coding code completion 2026_06_25 | 69.6%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 69.6 | coding |
| livebench coding code generation 2026_06_25 | 78.9%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 78.9 | coding |
| livebench coding javascript 2026_06_25 | 59.1%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 59.1 | coding |
| livebench coding python 2026_06_25 | 45%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 45.0 | coding |
| livebench coding typescript 2026_06_25 | 26.7%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 26.7 | coding |
| livebench math amps hard 2026_06_25 | 98%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 98.0 | math |
| livebench math integrals with game 2026_06_25 | 59%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 59.0 | math |
| livebench math math comp 2026_06_25 | 97.1%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 97.1 | math |
| livebench math olympiad 2026_06_25 | 86.9%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 86.9 | math |
| livebench math simplify 2026_06_25 | 64.1%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 64.1 | math |
| livebench reasoning connections 2026_06_25 | 96.5%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 96.5 | reasoning |
| livebench reasoning consecutive events 2026_06_25 | 71.8%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 71.8 | reasoning |
| livebench reasoning logic with navigation 2026_06_25 | 84%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 84.0 | reasoning |
| livebench reasoning spatial 2026_06_25 | 96%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 96.0 | reasoning |
| livebench reasoning theory of mind 2026_06_25 | 78.8%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 78.8 | reasoning |
| livebench reasoning zebra puzzle 2026_06_25 | 74.5%LiveBenchLiveBench subtask result; dataset edition 2026_06_25; publication time is HTTP Last-Modified of the score CSV, not the dataset edition or individual evaluation date [variant] 2026_06_25Published Oct 7, 2026
Retrieved Oct 8, 2026 · factual citation; Apache-2.0 code Open source ↗ | 74.5 | reasoning |
| otis mock aime 2024 2025 | 95.6%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Aug 7, 2026
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 95.6 | math |
| swe bench pro | 60.6%Official model cards via models.devLab-reported; metric resolve rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published May 19, 2026
Retrieved Oct 8, 2026 · factual citation; MIT transcription Open source ↗ | 60.6 | coding |
| swe bench verified | 77.3%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Jun 18, 2026
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 77.3 | coding |
| swe bench verified | 80.4%Official model cards via models.devLab-reported; metric resolved; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published May 19, 2026
Retrieved Oct 8, 2026 · factual citation; MIT transcription Open source ↗ | 80.4 | coding |
| terminal bench v2 0 | 69.7%Official model cards via models.devLab-reported; metric success rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] Terminus-2; 2.0Published May 19, 2026
Retrieved Oct 8, 2026 · factual citation; MIT transcription Open source ↗ | 69.7 | coding |
Normalization uses fixed absolute 0–100 scales for each unit, independently of other models that have a result on each benchmark. Coverage and evidence breadth still affect the composite; compare the evaluation conditions before reading a small score gap as decisive. “lab-reported” marks the provider's own published figure.
Sources: in and pending
in Reported (3)
- Epoch AI Benchmarking · arrived Oct 8, 2026
- Official model cards via models.dev · arrived Oct 8, 2026
- LiveBench · arrived Oct 8, 2026
pending Awaiting (4)
- ARC Prize · carries 13% of expected weight
- Humanity’s Last Exam · carries 13% of expected weight
- LMArena / Arena · carries 25% of expected weight
- Terminal-Bench · carries 6% of expected weight
The confidence % rises as pending sources publish. Some sources never cover some models — that is why 100% confidence arrives at 80% of expected weight, not at full coverage.