Qwen Turbo: Price, Context, Benchmarks, and Release Details
46.6
SI Score (method si-v3-retained-evidence-2)
47% confidence 47 percent, Low confidence — 1 of 4 expected sources in
Coverage 37% of expected source weight · 100% confidence at 80% coverage
Pillar breakdown
Coding (weight 40 percent) —
Math (weight 15 percent) 39.5
Preference (weight 15 percent) —
Reasoning (weight 30 percent) 41.8
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
- $0.05Alibaba Model Studio pricingOfficial Alibaba Model Studio International USD on-demand API; standard tier; . Output uses non-thinking rate when both modes are available; thinking-only products use their thinking rate. Cache, Batch, free quotas and regional rates excluded
Retrieved Oct 9, 2026 · factual citation
Open source ↗ - Output price / 1M
- $0.20Alibaba Model Studio pricingOfficial Alibaba Model Studio International USD on-demand API; standard tier; . Output uses non-thinking rate when both modes are available; thinking-only products use their thinking rate. Cache, Batch, free quotas and regional rates excluded
Retrieved Oct 9, 2026 · factual citation
Open source ↗ - Context window
- 1Mmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Max output
- 16.4Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Released
- Nov 1, 2024models.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Open weights
- Nomodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - License
- not yet reported
- Input modalities
- textmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗
Benchmark results
3 results| Benchmark | Raw result | Normalized (0–100) | Pillar |
|---|---|---|---|
| gpqa diamond | 41.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Apr 7, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 41.8 | reasoning |
| math level 5 | 56.2%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Apr 7, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 56.2 | math |
| otis mock aime 2024 2025 | 6.1%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Apr 7, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 6.1 | math |
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 (1)
- Epoch AI Benchmarking · arrived Oct 8, 2026
pending Awaiting (3)
- Official model cards via models.dev · carries 7% of expected weight
- LiveBench · carries 19% of expected weight
- LMArena / Arena · carries 37% 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.