Alibaba / Qwen, released Apr 17, 2026
Qwen3.6 35B-A3Bprice, context, benchmarks and release details
Provisional: not enough results to rank yet Open weights 37% confidence 37 percent, Low confidence, 2 of 7 expected sources in
56.3
SI Score
Not ranked yet
- Input, per 1M tokens
- $0.25Alibaba Model Studio pricingOfficial Alibaba Model Studio Global USD on-demand API; 0<Token≤256K; . 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, per 1M tokens
- $1.49Alibaba Model Studio pricingOfficial Alibaba Model Studio Global USD on-demand API; 0<Token≤256K; . 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
- 262Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Max output
- 65.5Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Released
- Apr 17, 2026models.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗
How the score breaks down
Coding (weight 40 percent) 73.4
Math (weight 15 percent) 38.6
Preference (weight 15 percent) —
Reasoning (weight 30 percent) 84.3
Weights: reasoning 30%, math 15%, coding 40%, preference 15%. Results use fixed 0–100 scales before averaging, and thin evidence is pulled toward 50. Method si-v3-retained-evidence-2, computed Oct 9, 2026, 06:15 UTC.
Around it on the leaderboard
- 1 Claude Fable 5.1 80.2
- 2 Claude Opus 5.5 78.4
- 3 GPT-6 Astra 77.5
- 4 Claude Fable 5 76.8
- 5 Claude Opus 5 74.9
Benchmark results
4 benchmarks, 7 resultsEach row shows the best published result. Where a model was tested at several settings, such as reasoning effort, open the row to see each one. Hover or tap a value for its source.
GPQA Diamondreasoning 84.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] nonePublished Aug 7, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
84.8 2 settings
- no reasoning 84.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] nonePublished Aug 7, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - Setting 2 83.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Aug 7, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
FrontierMath Tiers 1–3 (v2)math 20.4%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] nonePublished Aug 30, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
20.4 2 settings
- no reasoning 20.4%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] nonePublished Aug 30, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - Setting 2 17.5%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Aug 29, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
OTIS Mock AIME 2024–2025math 86.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Aug 7, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
86.7 2 settings
- no reasoning 68.9%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] nonePublished Aug 7, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - Setting 2 86.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Aug 7, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
SWE-bench Verifiedcoding 73.4%Official model cards via models.devLab-reported; metric resolved; transcribed by MIT models.dev catalog; not independently evaluated [variant]
Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗ 73.4
Open source ↗ 73.4
Normalization uses a fixed 0–100 scale for each unit, independent of other models. Compare evaluation conditions before reading a small gap as decisive. “Lab-reported” marks the provider's own published figure.
Details and sources
- Open weights
- Yesmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - License
- not yet reported
- Input modalities
- text, image, video, audiomodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - First seen by SuperIndex
- Oct 8, 2026
- Coverage
- 30% of expected source weight
Reported (2)
- Epoch AI BenchmarkingOct 8, 2026
- Official model cards via models.devOct 8, 2026
Awaiting (5)
- ARC Prize13% of weight
- Humanity’s Last Exam13% of weight
- LiveBench13% of weight
- LMArena / Arena25% of weight
- Terminal-Bench6% of weight
Confidence rises as pending sources publish. Some sources never cover some models, so confidence reaches 100% at 80% of expected weight.