Alibaba / Qwen, released Jul 21, 2025
Qwen3 235B-A22B Instruct 2507price, context, benchmarks and release details
Provisional: not enough results to rank yet Open weights 48% confidence 48 percent, Low confidence, 2 of 7 expected sources in
42.4
SI Score
Not ranked yet
- Input, per 1M tokens
- $0.23Alibaba Model Studio pricingOfficial Alibaba Model Studio Global USD on-demand API; standard tier; Non-Thinking mode only. 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
- $0.92Alibaba Model Studio pricingOfficial Alibaba Model Studio Global USD on-demand API; standard tier; Non-Thinking mode only. 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
- 16.4Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Released
- Jul 21, 2025models.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗
How the score breaks down
Coding (weight 40 percent) —
Math (weight 15 percent) —
Preference (weight 15 percent) 74.9
Reasoning (weight 30 percent) 7.5
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
5 benchmarks, 5 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.
ARC-AGI-1 (public eval)reasoning 17%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] qwen3-235b-a22b-instruct-2507Published Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation
Open source ↗ 17.0
Open source ↗ 17.0
ARC-AGI-1 (semi-private)reasoning 11%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] qwen3-235b-a22b-instruct-2507Published Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation
Open source ↗ 11.0
Open source ↗ 11.0
ARC-AGI-2 (public eval)reasoning 0.8%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] qwen3-235b-a22b-instruct-2507Published Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation
Open source ↗ 0.8
Open source ↗ 0.8
ARC-AGI-2 (semi-private)reasoning 1.3%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] qwen3-235b-a22b-instruct-2507Published Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation
Open source ↗ 1.3
Open source ↗ 1.3
LMArena Textpreference 1419.2 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 8, 2026
Retrieved Oct 9, 2026 · CC-BY-4.0
Open source ↗ 74.9
Open source ↗ 74.9
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
- Apache 2.0models.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Input modalities
- textmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - First seen by SuperIndex
- Oct 8, 2026
- Coverage
- 38% of expected source weight
Reported (2)
- ARC PrizeOct 8, 2026
- LMArena / ArenaOct 8, 2026
Awaiting (5)
- Epoch AI Benchmarking25% of weight
- Humanity’s Last Exam13% of weight
- Official model cards via models.dev4% of weight
- LiveBench13% 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.