whatmodel.sh / GPT-5.5 Pro
GPT-5.5 Pro
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GPT-5.5 Pro is a closed-weights language model from OpenAI, released April 2026.
Catalogue identity notes
Comparison-only. Epoch model_metadata.csv: model group 'GPT-5.5 Pro', OpenAI, date 2026-04-23, 'API access'. Pre-release rows (gpt-5.5-pro-pre-release_*) are a separate model version and are not used.
$ how it compares
The LLMs ranked either side of GPT-5.5 Pro, out of 828 with a score, then more from OpenAI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 6 |  Claude Opus 5 | 83±1 | api $10.00/M | ||
| 7 | GPT-6 Sol | 83±1 | api $4.00/M | ||
| 8 | GPT-5.5 Pro | 82±1 | api $67.50/M | ||
| 9 |  Gemini 4 Argon (limited preview) | 82±1 | limited preview | ||
| 10 | GPT-5.6 Sol | 82±1 | api $8.00/M | ||
| more from OpenAI | |||||
| 1 | GPT-6 Astra | 87±1 | api $20.00/M | ||
| 3 | GPT-6.1 Sol | 85±1 | api $4.00/M | ||
| 13 | GPT-5.4 Pro | 81±1 | api $67.50/M | ||
$ what it's good at
agent tasks▁▁▃▆█▆▅▅▅▄▃▁ #15 of 300 * · 17% of its score · 2 tests
Trails GLM-5.3, Claude Haiku 5.5, DeepSeek V4.1 Flash here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| BrowseComp lab | 90.1% ✎ | ▄··█▄··▄·▄█▄ beats 6/8 | 9.3% |
| Humanity's Last Exam with tools lab | 57.2% ✎ | ▃·▃·▅····▅·█ beats 6/8 | 7.4% |
math▁▂▃▅▆▅▄█▄▁▃▁ #10 of 398 * · 68% of its score · 2 tests
Beats Claude Opus 5 here, though they rank higher overall. Trails Claude Mythos Preview (limited preview), Claude Fable 5, GPT-5.6 Sol here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| FrontierMath tier 4 v2 | 78.0% ✎ | ▁▂▁▂█▅▂▃▁▁▁▁ beats 55/63 | 42% |
| FrontierMath tiers 1–3 v2 | 87.7% ✎ | ▂··▁▁▃▄▄█▅▃▃ beats 75/82 | 26% |
web search▂▁▂▆▄█▃▃▁▁·▁ #14 of 80 * · 17% of its score · 2 tests
Beats Claude Fable 5.1, Claude Sonnet 5.5, Claude Opus 5 here, though they rank higher overall. Trails GPT-4o mini (2024-07-18), Mistral Small 3.1 24B (2503), GPT-4o (2024-08-06) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| BrowseComp lab | 90.1% ✎ | ▄··█▄··▄·▄█▄ beats 6/8 | 9.3% |
| Humanity's Last Exam with tools lab | 57.2% ✎ | ▃·▃·▅····▅·█ beats 6/8 | 7.4% |
other tests1 not in a task · 14% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Humanity's Last Exam lab | 43.1% ✎ | ▃··▃·▃▃···▃█ beats 7/7 | 14% |
$ where to run it
Through an API
api · $67.50/M · $30 in / $180 out per 1M tokens · 1.1M context · developers.openai.com ↗ · price sourceUSD per 1M tokens, OpenAI API standard processing for gpt-5.5-pro (Responses API; also Batch): input $30.00, output $180.00; no cached-input discount; Batch API pricing and the 10% regional-processing (data residency) uplift excluded. Currency: USD. OpenAI official model page, observed 2026-09-25.
$ how the score was worked out · Epoch AI — internal evaluatio… 68%, OpenAI vendor-reported benchm… 31%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 Epoch AI — internal evaluations | 2 | 68% |
 OpenAI vendor-reported benchmark scores for its own models (openai.com announcement posts and Deployment Safety Hub system cards) · lab-reported | 3 | 31% |
| prior · pull toward the middle | 1% | |
| weighted average | = 82.1 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json