GLM-4.6: Price, Context, Benchmarks, and Release Details

Z.ai provisional listing open weights · first seen Oct 8, 2026 · score computed Oct 9, 2026, 01:50 UTC
47.8
SI Score (method si-v3-retained-evidence-2)
#112 of 140 ranked
55% confidence 55 percent, Medium confidence — 4 of 9 expected sources in
Coverage 44% of expected source weight · 100% confidence at 80% coverage

Pillar breakdown

Coding (weight 40 percent) 33.0
Math (weight 15 percent) —
Preference (weight 15 percent) 76.8
Reasoning (weight 30 percent) —

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
$0.60Z.ai API pricingOfficial Z.ai global Standard uncached token rate; coding-plan subscriptions, cache and Batch excluded Retrieved Oct 9, 2026 · factual citation
Open source ↗
Output price / 1M
$2.20Z.ai API pricingOfficial Z.ai global Standard uncached token rate; coding-plan subscriptions, cache and Batch excluded Retrieved Oct 9, 2026 · factual citation
Open source ↗
Context window
205Kmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Max output
131Kmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Released
Sep 30, 2025models.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Open weights
Yesmodels.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
lmarena text 1439.8 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 2, 2026 Retrieved Oct 9, 2026 · CC-BY-4.0
Open source ↗
76.8 preference
swe bench pro public 9.7%Official model cards via models.devLab-reported; metric resolve rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] public Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
9.7 coding
swe bench pro public 9.7%SWE-bench Pro (public)Published steward score [variant] Published Jan 27, 2026 Retrieved Oct 9, 2026 · factual citation
Open source ↗
9.7 coding
swe bench verified 55.4%SWE-bench VerifiedSWE-bench published model plus agent result; harness retained, not a base model evaluation [variant] mini-SWE-agent; 1.17.1Published Dec 1, 2025 Retrieved Oct 9, 2026 · factual citation
Open source ↗
55.4 coding
swe bench verified 68.2%SWE-bench VerifiedSWE-bench published model plus agent result; harness retained, not a base model evaluation [variant] UndisclosedPublished Sep 30, 2025 Retrieved Oct 9, 2026 · factual citation
Open source ↗
68.2 coding
terminal bench 25%Official model cards via models.devLab-reported; metric success rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published May 22, 2026 Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
25.0 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 (4)

  • Official model cards via models.dev · arrived Oct 8, 2026
  • LMArena / Arena · arrived Oct 8, 2026
  • SWE-bench Verified · arrived Oct 8, 2026
  • SWE-bench Pro (public) · arrived Oct 8, 2026

pending Awaiting (5)

  • ARC Prize · carries 10% of expected weight
  • Epoch AI Benchmarking · carries 20% of expected weight
  • Humanity’s Last Exam · carries 10% of expected weight
  • LiveBench · carries 10% of expected weight
  • Terminal-Bench · carries 5% 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.