GPT-5: Price, Context, Benchmarks, and Release Details
55.7
SI Score (method si-v3-retained-evidence-2)
#83 of 140 ranked
100% confidence 100 percent, Full confidence — 8 of 10 expected sources in
Coverage 86% of expected source weight · 100% confidence at 80% coverage
Pillar breakdown
Coding (weight 40 percent) 66.7
Math (weight 15 percent) 55.6
Preference (weight 15 percent) 73.7
Reasoning (weight 30 percent) 35.2
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
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
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
- Aug 7, 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
35 results| Benchmark | Raw result | Normalized (0–100) | Pillar |
|---|---|---|---|
| aider polyglot | 81.3%Aider polyglotPublished source fact [variant] Aider polyglot; 225 cases; 2 attemptsPublished Aug 25, 2025
Retrieved Oct 9, 2026 · Apache-2.0 Open source ↗ | 81.3 | coding |
| arc agi v1 public eval | 65.9%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-5-2025-08-07-highPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 65.9 | reasoning |
| arc agi v1 public eval | 48.4%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-5-2025-08-07-lowPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 48.4 | reasoning |
| arc agi v1 public eval | 63.4%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-5-2025-08-07-mediumPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 63.4 | reasoning |
| arc agi v1 semi private | 65.7%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-5-2025-08-07-highPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 65.7 | reasoning |
| arc agi v1 semi private | 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-5-2025-08-07-lowPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 44.0 | reasoning |
| arc agi v1 semi private | 56.2%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-5-2025-08-07-mediumPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 56.2 | reasoning |
| arc agi v2 public eval | 9.6%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-5-2025-08-07-highPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 9.6 | reasoning |
| arc agi v2 public eval | 2.5%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-5-2025-08-07-lowPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 2.5 | reasoning |
| arc agi v2 public eval | 7.6%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-5-2025-08-07-mediumPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 7.6 | reasoning |
| arc agi v2 semi private | 9.9%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-5-2025-08-07-highPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 9.9 | 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-5-2025-08-07-lowPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 1.9 | reasoning |
| arc agi v2 semi private | 7.5%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-5-2025-08-07-mediumPublished Oct 6, 2026
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 7.5 | reasoning |
| frontiermath tier 4 v2 | 22.0%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Jun 11, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 22.0 | math |
| frontiermath tiers 1 3 v2 | 55.4%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Jun 10, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 55.4 | math |
| frontiermath tiers 1 3 v2 | 37.2%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] lowPublished Aug 27, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 37.2 | math |
| frontiermath tiers 1 3 v2 | 18.2%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] minimalPublished Aug 27, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 18.2 | math |
| gpqa diamond | 86.2%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Oct 29, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 86.2 | reasoning |
| gpqa diamond | 85.4%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] mediumPublished Aug 7, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 85.4 | reasoning |
| gpqa diamond | 71.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] minimalPublished Jul 20, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 71.7 | reasoning |
| hle scale | 25.3%Humanity’s Last ExamPotential contamination warning: This model was evaluated after the public release of HLE, allowing model builder access to the prompts and solutions. Sampled at reasoning_effort: 'high'. [variant] Published Aug 7, 2025
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 25.3 | reasoning |
| lmarena text | 1406.1 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 2, 2026
Retrieved Oct 9, 2026 · CC-BY-4.0 Open source ↗ | 73.7 | preference |
| math level 5 | 98.1%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Oct 29, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 98.1 | math |
| math level 5 | 97.9%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] mediumPublished Aug 20, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 97.9 | math |
| otis mock aime 2024 2025 | 91.4%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Oct 29, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 91.4 | math |
| otis mock aime 2024 2025 | 87.2%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] mediumPublished Aug 7, 2025
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 87.2 | math |
| otis mock aime 2024 2025 | 46.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] minimalPublished Jul 20, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 46.7 | math |
| swe bench pro public | 41.8%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 ↗ | 41.8 | coding |
| swe bench pro public | 41.8%SWE-bench Pro (public)Published steward score [variant] Published Nov 26, 2025
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 41.8 | coding |
| swe bench verified | 73.6%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Feb 6, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 73.6 | coding |
| swe bench verified | 71.5%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] mediumPublished Feb 5, 2026
Retrieved Oct 9, 2026 · CC-BY Open source ↗ | 71.5 | coding |
| swe bench verified | 65%SWE-bench VerifiedSWE-bench published model plus agent result; harness retained, not a base model evaluation [variant] mini-SWE-agent; medium; 1.7.0Published Aug 7, 2025
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 65.0 | coding |
| swe bench verified | 71.8%SWE-bench VerifiedSWE-bench published model plus agent result; harness retained, not a base model evaluation [variant] OpenHandsPublished Aug 7, 2025
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 71.8 | coding |
| swe bench verified | 71.2%SWE-bench VerifiedSWE-bench published model plus agent result; harness retained, not a base model evaluation [variant] Prometheus-v1.2Published Sep 29, 2025
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 71.2 | coding |
| swe bench verified | 74.4%SWE-bench VerifiedSWE-bench published model plus agent result; harness retained, not a base model evaluation [variant] Prometheus-v1.2.1Published Oct 15, 2025
Retrieved Oct 9, 2026 · factual citation Open source ↗ | 74.4 | 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 (8)
- Aider polyglot · arrived Oct 8, 2026
- ARC Prize · arrived Oct 8, 2026
- Epoch AI Benchmarking · arrived Oct 8, 2026
- Humanity’s Last Exam · arrived Oct 8, 2026
- 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 (2)
- 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.