SI Index Data as of Oct 8, 2026

GLM-4.7: Price, Context, Benchmarks, and Release Details

Z.ai provisional listing open weights · first seen Oct 8, 2026 · score computed Oct 8, 2026, 22:13 UTC
61.3
SI Score (method si-v2-absolute-shrinkage-1)
#54 of 115 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) 53.6
Math (weight 15 percent) 83.3
Preference (weight 15 percent) 76.5
Reasoning (weight 30 percent) 83.3

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
205Kmodels.devPublished source fact Retrieved Oct 8, 2026 · MIT
Open source ↗
Max output
131Kmodels.devPublished source fact Retrieved Oct 8, 2026 · MIT
Open source ↗
Released
Dec 22, 2025models.devPublished source fact Retrieved Oct 8, 2026 · MIT
Open source ↗
Open weights
Yesmodels.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

5 results
Benchmark Raw result Normalized (0–100) Pillar
gpqa diamond 83.3%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Jan 29, 2026 Retrieved Oct 8, 2026 · CC-BY
Open source ↗ 		83.3 reasoning
lmarena text 1435.5 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 2, 2026 Retrieved Oct 8, 2026 · CC-BY-4.0
Open source ↗ 		76.5 preference
otis mock aime 2024 2025 83.3%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Jan 29, 2026 Retrieved Oct 8, 2026 · CC-BY
Open source ↗ 		83.3 math
swe bench verified 73.8%Official model cards via models.devLab-reported; metric resolved; transcribed by MIT models.dev catalog; not independently evaluated [variant] Retrieved Oct 8, 2026 · factual citation; MIT transcription
Open source ↗ 		73.8 coding
terminal bench 33.4%Official model cards via models.devLab-reported; metric score; transcribed by MIT models.dev catalog; not independently evaluated [variant] Retrieved Oct 8, 2026 · factual citation; MIT transcription
Open source ↗ 		33.4 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.