Qwen3.6 Max Preview: Price, Context, Benchmarks, and Release Details
66.4
SI Score (method si-v2-absolute-shrinkage-1)
#26 of 115 ranked
64% confidence 64 percent, Medium confidence — 2 of 7 expected sources in
Coverage 51% of expected source weight · 100% confidence at 80% coverage
Pillar breakdown
Coding (weight 40 percent) 76.7
Math (weight 15 percent) 91.1
Preference (weight 15 percent) 77.5
Reasoning (weight 30 percent) 87.4
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
- 262Kmodels.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
- Apr 20, 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
4 results| Benchmark | Raw result | Normalized (0–100) | Pillar |
|---|---|---|---|
| gpqa diamond | 87.4%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 ↗ | 87.4 | reasoning |
| lmarena text | 1446.8 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 2, 2026
Retrieved Oct 8, 2026 · CC-BY-4.0 Open source ↗ | 77.5 | preference |
| otis mock aime 2024 2025 | 91.1%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 ↗ | 91.1 | math |
| swe bench verified | 76.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published May 28, 2026
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 76.7 | 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 (2)
- Epoch AI Benchmarking · arrived Oct 8, 2026
- LMArena / Arena · arrived Oct 8, 2026
pending Awaiting (5)
- ARC Prize · carries 13% of expected weight
- Humanity’s Last Exam · carries 13% of expected weight
- Official model cards via models.dev · carries 4% of expected weight
- LiveBench · carries 13% 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.