SI Index Data as of Oct 9, 2026

Qwen3 Max: Price, Context, Benchmarks, and Release Details

Alibaba / Qwen provisional listing · first seen Oct 8, 2026 · score computed Oct 9, 2026, 01:50 UTC
50.2
SI Score (method si-v3-retained-evidence-2)
#107 of 140 ranked
69% confidence 69 percent, Medium confidence — 3 of 7 expected sources in
Coverage 55% of expected source weight · 100% confidence at 80% coverage

Pillar breakdown

Coding (weight 40 percent) 20.5
Math (weight 15 percent) 61.1
Preference (weight 15 percent) 74.3
Reasoning (weight 30 percent) 72.6

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
$0.36Alibaba Model Studio pricingOfficial Alibaba Model Studio Global USD on-demand API; 0<Token≤32K; 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 price / 1M
$1.43Alibaba Model Studio pricingOfficial Alibaba Model Studio Global USD on-demand API; 0<Token≤32K; 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
65.5Kmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Released
Sep 23, 2025models.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

6 results
Benchmark Raw result Normalized (0–100) Pillar
frontiermath tiers 1 3 v2 18.9%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Aug 30, 2026 Retrieved Oct 9, 2026 · CC-BY
Open source ↗ 		18.9 math
gpqa diamond 72.6%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Oct 6, 2025 Retrieved Oct 9, 2026 · CC-BY
Open source ↗ 		72.6 reasoning
lmarena text 1412.4 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 2, 2026 Retrieved Oct 9, 2026 · CC-BY-4.0
Open source ↗ 		74.3 preference
math level 5 97.1%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Oct 9, 2025 Retrieved Oct 9, 2026 · CC-BY
Open source ↗ 		97.1 math
otis mock aime 2024 2025 73.3%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Oct 6, 2025 Retrieved Oct 9, 2026 · CC-BY
Open source ↗ 		73.3 math
terminal bench 20.5%Official model cards via models.devLab-reported; metric success rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published May 30, 2026 Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗ 		20.5 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
  • LMArena / Arena · arrived Oct 8, 2026

pending Awaiting (4)

  • ARC Prize · carries 13% of expected weight
  • Humanity’s Last Exam · carries 13% 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.