SI Index Data as of Oct 8, 2026

GPT-5.5 Pro: Price, Context, Benchmarks, and Release Details

OpenAI · first seen Oct 8, 2026 · score computed Oct 8, 2026, 22:13 UTC
63.5
SI Score (method si-v2-absolute-shrinkage-1)
#38 of 115 ranked
53% confidence 53 percent, Medium confidence — 3 of 7 expected sources in
Coverage 43% of expected source weight · 100% confidence at 80% coverage

Pillar breakdown

Coding (weight 40 percent) —
Math (weight 15 percent) 73.3
Preference (weight 15 percent) —
Reasoning (weight 30 percent) 85.9

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
1.1Mmodels.devPublished source fact Retrieved Oct 8, 2026 · MIT
Open source ↗
Max output
128Kmodels.devPublished source fact Retrieved Oct 8, 2026 · MIT
Open source ↗
Released
Apr 24, 2026OpenAI API changelogPublished source fact Retrieved Oct 8, 2026 · factual citation
Open source ↗
Open weights
Nomodels.devPublished source fact Retrieved Oct 8, 2026 · MIT
Open source ↗
License
not yet reported
Input modalities
text, image, pdfmodels.devPublished source fact Retrieved Oct 8, 2026 · MIT
Open source ↗

Benchmark results

14 results
Benchmark Raw result Normalized (0–100) Pillar
arc agi v1 public eval 98%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-5-pro-2026-04-23-highPublished Oct 6, 2026 Retrieved Oct 8, 2026 · factual citation
Open source ↗ 		98.0 reasoning
arc agi v1 public eval 98.0%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-5-pro-2026-04-23-xhighPublished Oct 6, 2026 Retrieved Oct 8, 2026 · factual citation
Open source ↗ 		98.0 reasoning
arc agi v1 semi private 96.5%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-5-pro-2026-04-23-highPublished Oct 6, 2026 Retrieved Oct 8, 2026 · factual citation
Open source ↗ 		96.5 reasoning
arc agi v1 semi private 95%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-5-pro-2026-04-23-xhighPublished Oct 6, 2026 Retrieved Oct 8, 2026 · factual citation
Open source ↗ 		95.0 reasoning
arc agi v2 public eval 90.1%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-5-pro-2026-04-23-highPublished Oct 6, 2026 Retrieved Oct 8, 2026 · factual citation
Open source ↗ 		90.1 reasoning
arc agi v2 public eval 90.4%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-5-pro-2026-04-23-xhighPublished Oct 6, 2026 Retrieved Oct 8, 2026 · factual citation
Open source ↗ 		90.4 reasoning
arc agi v2 semi private 84.6%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-5-pro-2026-04-23-highPublished Oct 6, 2026 Retrieved Oct 8, 2026 · factual citation
Open source ↗ 		84.6 reasoning
arc agi v2 semi private 84.2%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-5-pro-2026-04-23-xhighPublished Oct 6, 2026 Retrieved Oct 8, 2026 · factual citation
Open source ↗ 		84.2 reasoning
frontiermath tier 1 3 52.4%Official model cards via models.devLab-reported; metric accuracy; transcribed by MIT models.dev catalog; not independently evaluated [variant] Tier 1-3Published Apr 23, 2026 Retrieved Oct 8, 2026 · factual citation; MIT transcription
Open source ↗ 		52.4 math
frontiermath tier 4 39.6%Official model cards via models.devLab-reported; metric accuracy; transcribed by MIT models.dev catalog; not independently evaluated [variant] Tier 4Published Apr 23, 2026 Retrieved Oct 8, 2026 · factual citation; MIT transcription
Open source ↗ 		39.6 math
frontiermath tier 4 v2 78.0%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] xhighPublished Jun 12, 2026 Retrieved Oct 8, 2026 · CC-BY
Open source ↗ 		78.0 math
frontiermath tiers 1 3 v2 87.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] xhighPublished Jun 12, 2026 Retrieved Oct 8, 2026 · CC-BY
Open source ↗ 		87.7 math
hle 43.1%Official model cards via models.devLab-reported; metric accuracy; transcribed by MIT models.dev catalog; not independently evaluated [variant] no toolsPublished Apr 23, 2026 Retrieved Oct 8, 2026 · factual citation; MIT transcription
Open source ↗ 		43.1 reasoning
hle tools 57.2%Official model cards via models.devLab-reported; metric accuracy; transcribed by MIT models.dev catalog; not independently evaluated [variant] with toolsPublished Apr 23, 2026 Retrieved Oct 8, 2026 · factual citation; MIT transcription
Open source ↗ 		57.2 reasoning

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)

  • ARC Prize · arrived Oct 8, 2026
  • Epoch AI Benchmarking · arrived Oct 8, 2026
  • Official model cards via models.dev · arrived Oct 8, 2026

pending Awaiting (4)

  • Humanity’s Last Exam · carries 13% of expected weight
  • LiveBench · carries 13% of expected weight
  • LMArena / Arena · carries 25% 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.