GPT-5.2 Codex (Xhigh)
77.6%
no interval published
aiqbench · LLM benchmarks
Benchmarks
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- 8
- Entries tracked
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Filter by org:
Released:
GPT-5.4 nano (Xhigh)
75.9%
no interval published
GPT-5.5 (Xhigh)
75.9%
no interval published
One grid, every column. Pick axes in the Axes panel — one axis ranks like a single board, several blend into a weighted composite, all of them read as the index. Rank is assigned before any filter and never renumbers.
| Model | Org | ± CI | Swing | Add to compare | |||||
|---|---|---|---|---|---|---|---|---|---|
| 14 | GPT-5.2 Codex (Xhigh) | openai | Oct 2026 | $1.75 / $14 | 77.6% | — | #37–370 | ||
| 22 | GPT-5.4 nano (Xhigh) | openai | Oct 2026 | $0.20 / $1.25 | 75.9% | — | #94–222 | ||
| 24 | GPT-5.5 (Xhigh) | openai | Sep 2026 | $5 / $30 | 75.9% | — | #9–34 | ||
| 29 | GPT-5 mini (High) | openai | Aug 2025 | $0.25 / $2 | 75.4% | — | #126–343 | ||
| 30 | GPT-5.2 (Xhigh) | openai | Dec 2025 | $1.75 / $14 | 75.4% | — | #34–93 | ||
| 31 | GPT-5.3 Codex (Xhigh) | openai | Oct 2026 | $1.75 / $14 | 75.4% | — | #29–236 | ||
| 33 | GPT-5 Codex (High) | openai | — | — | 74.1% | — | #17–142 | ||
| 35 | GPT-5.4 (Xhigh) | openai | Sep 2026 | $2.50 / $15 | 73.9% | — | #12–27 | ||
| 38 | GPT-5.4 mini (Xhigh) | openai | Oct 2026 | $0.75 / $4.50 | 73.3% | — | #75–180 | ||
| 40 | GPT-5 (High) | openai | Aug 2025 | $1.25 / $10 | 73.1% | — | #64–149 | ||
| 41 | GPT-5.1 (High) | openai | Nov 2025 | $1.25 / $10 | 72.9% | — | #45–242 | ||
| 42 | GPT-5.6 Sol (Max) | openai | Sep 2026 | $4 / $20 | 72.7% | — | #6–24 | ||
| 49 | GPT-5.5 Instant (May 2026) | openai | Sep 2026 | — | 71.5% | — | #52–114 | ||
| 52 | GPT-5.6 Terra (Max) | openai | Sep 2026 | $2 / $12 | 71.2% | — | #13–60 | ||
| 60 | GPT-5.1 Codex (High) | openai | Oct 2026 | $1.25 / $10 | 70.0% | — | #53–375 | ||
| 71 | GPT-5.1 Codex mini (High) | openai | Oct 2026 | $0.25 / $2 | 67.9% | — | #54–425 | ||
| 72 | GPT-5 nano (High) | openai | Aug 2025 | $0.05 / $0.40 | 67.6% | — | #132–399 |
Showing IFBench (Artificial Analysis) exactly as the source published it — its own score and confidence interval, no blending. Select a second axis to build a weighted composite.