GPT-4o (2024-05-13): Price, Context, Benchmarks, and Release Details
49.4
SI Score (method si-v2-absolute-shrinkage-1)
#96 of 115 ranked
75% confidence 75 percent, Medium confidence — 1 of 3 expected sources in
Coverage 60% of expected source weight · 100% confidence at 80% coverage
Pillar breakdown
Coding (weight 40 percent) —
Math (weight 15 percent) 36.1
Preference (weight 15 percent) 62.3
Reasoning (weight 30 percent) 48.9
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
- 128Kmodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - Max output
- 4.1Kmodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - Released
- May 13, 2024models.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - Open weights
- Nomodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - License
- not yet reported
- Input modalities
- text, imagemodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗
Benchmark results
4 results| Benchmark | Raw result | Normalized (0–100) | Pillar |
|---|---|---|---|
| gpqa diamond | 48.9%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Jan 27, 2025
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 48.9 | reasoning |
| lmarena text | 1300.4 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 2, 2026
Retrieved Oct 8, 2026 · CC-BY-4.0 Open source ↗ | 62.3 | preference |
| math level 5 | 51.0%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Jan 27, 2025
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 51.0 | math |
| otis mock aime 2024 2025 | 6.3%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Feb 25, 2025
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 6.3 | math |
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 (1)
- LMArena / Arena · arrived Oct 8, 2026
pending Awaiting (2)
- Official model cards via models.dev · carries 10% of expected weight
- LiveBench · carries 30% 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.