GPT-5.1: Price, Context, Benchmarks, and Release Details
55.7
SI Score (method si-v3-retained-evidence-2)
#84 of 140 ranked
85% confidence 85 percent, High confidence — 4 of 8 expected sources in
Coverage 68% of expected source weight · 100% confidence at 80% coverage
Pillar breakdown
Coding (weight 40 percent) 67.3
Math (weight 15 percent) 69.0
Preference (weight 15 percent) 75.2
Reasoning (weight 30 percent) 32.6
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
- $1.25models.devFirst-party hosted API; MIT models.dev transcription. Provider documentation: https://platform.openai.com/docs/models. Exact canonical endpoint; lowest short-context Standard USD token tier; cache/batch discounts excluded. Deprecated endpoints excluded; openai/gpt-5.1
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Output price / 1M
- $10.00models.devFirst-party hosted API; MIT models.dev transcription. Provider documentation: https://platform.openai.com/docs/models. Exact canonical endpoint; lowest short-context Standard USD token tier; cache/batch discounts excluded. Deprecated endpoints excluded; openai/gpt-5.1
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Context window
- 400Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Max output
- 128Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Released
- Nov 13, 2025OpenAI API changelogPublished source fact
Retrieved Oct 9, 2026 · factual citation
Open source ↗ - Open weights
- Nomodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - License
- not yet reported
- Input modalities
- text, imagemodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗
Benchmark results
26 results| Benchmark | Raw result | Normalized (0–100) | Pillar |
|---|---|---|---|
| arc agi v1 public eval | 77.1%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-5-1-2025-11-13-thinking-highPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 77.1 | reasoning |
| arc agi v1 public eval | 44%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-5-1-2025-11-13-thinking-lowPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 44.0 | reasoning |
| arc agi v1 public eval | 68.9%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-5-1-2025-11-13-thinking-mediumPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 68.9 | reasoning |
| arc agi v1 public eval | 12.4%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-5-1-2025-11-13-thinking-nonePublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 12.4 | reasoning |
| arc agi v1 semi private | 72.8%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-1-2025-11-13-thinking-highPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 72.8 | reasoning |
| arc agi v1 semi private | 33.2%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-5-1-2025-11-13-thinking-lowPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 33.2 | reasoning |
| arc agi v1 semi private | 57.7%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-5-1-2025-11-13-thinking-mediumPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 57.7 | reasoning |
| arc agi v1 semi private | 5.8%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-1-2025-11-13-thinking-nonePublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 5.8 | reasoning |
| arc agi v2 public eval | 18.3%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] gpt-5-1-2025-11-13-thinking-highPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 18.3 | reasoning |
| arc agi v2 public eval | 2.2%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-5-1-2025-11-13-thinking-lowPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 2.2 | reasoning |
| arc agi v2 public eval | 8.4%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-5-1-2025-11-13-thinking-mediumPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 8.4 | reasoning |
| arc agi v2 public eval | 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-5-1-2025-11-13-thinking-nonePublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 0.0 | reasoning |
| arc agi v2 semi private | 17.6%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-5-1-2025-11-13-thinking-highPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 17.6 | reasoning |
| arc agi v2 semi private | 1.9%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-5-1-2025-11-13-thinking-lowPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 1.9 | reasoning |
| arc agi v2 semi private | 6.5%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-5-1-2025-11-13-thinking-mediumPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 6.5 | reasoning |
| arc agi v2 semi private | 0.4%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-5-1-2025-11-13-thinking-nonePublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 0.4 | reasoning |
| gpqa diamond | 87.6%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Nov 13, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 87.6 | reasoning |
| gpqa diamond | 85.0%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] mediumPublished Nov 17, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 85.0 | reasoning |
| gpqa diamond | 66.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] nonePublished Aug 7, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 66.7 | reasoning |
| lmarena text | 1422.2 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 2, 2026
Retrieved Oct 9, 2026 · CC-BY-4.0 Open source ↗ | 75.2 | preference |
| otis mock aime 2024 2025 | 88.6%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Nov 13, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 88.6 | math |
| otis mock aime 2024 2025 | 63.9%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] lowPublished Nov 25, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 63.9 | math |
| otis mock aime 2024 2025 | 85.6%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] mediumPublished Nov 17, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 85.6 | math |
| otis mock aime 2024 2025 | 37.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] nonePublished Aug 7, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 37.8 | math |
| swe bench verified | 68.0%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Feb 18, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 68.0 | coding |
| swe bench verified | 66%SWE-bench VerifiedSWE-bench published model plus agent result; harness retained, not a base model evaluation [variant] mini-SWE-agent; medium; 1.15.0Published Nov 20, 2025
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 66.0 | 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)
- ARC Prize · arrived Oct 8, 2026
- Epoch AI Benchmarking · arrived Oct 8, 2026
- LMArena / Arena · arrived Oct 8, 2026
- SWE-bench Verified · arrived Oct 8, 2026
pending Awaiting (4)
- Humanity’s Last Exam · carries 11% of expected weight
- Official model cards via models.dev · carries 4% of expected weight
- LiveBench · carries 11% 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.