THE FORGE RANKINGS·2026-08-30

GPT-5.5

OpenAI
10
RANK of 37 ranked
FERROX INDEX81.6 weighted mean of category positions
GRADEA front rank
EVIDENCE11 / 32 benchmarks measured
QUALITYTHIN Correlated evidence in: preference. Those category scores rest on benchmarks from a single family.

Not separable from GPT-5.6 Terra at the measured 1.78 point band. The order is printed; the gap is not claimed.

Compared to what

Each rail is one classification. Every ranked model measured on it is a tick; this model is the orange marker. A hatched rail means this model has no measurement in that classification and its weight was redistributed.

Agents

3of 37 measured
53.1
13.1median 38.963.0

Coding

22of 37 measured
47.5
22.4median 50.070.7

Reasoning

14of 37 measured
72.8
18.1median 62.893.0

Preference

11of 15 measured
69.8
53.1median 71.882.5

Every measurement, and the field behind it

One card per benchmark this model has been measured on. The rail shows where it sits against every other ranked model measured on the same benchmark. Where the evaluator keys on a harness, the harness is named, because the same model scores differently under a different one.

LMArena Text (style-controlled)

5/20
1477 best 1507 · Claude Fable 5
LMArena CC-BY-4.0

FrontierCode

7/17
43.0% best 53.5% · Claude Fable 5
cognition.com CC-BY-4.0 codex xhigh

LMArena WebDev

12/16
1458 best 1626 · Claude Fable 5
LMArena CC-BY-4.0

Not measured

21/32
  • FrontierMath
  • SWE-bench Verified (Epoch's own run)
  • Humanity's Last Exam
  • METR time horizons
  • Terminal-Bench 4.0
  • Aider Polyglot
  • OSWorld
  • Cybench
  • BrowseComp
  • IMOAnswerBench
  • SWE-bench Multilingual
  • AIME
  • CyberGym
  • HMMT
  • MCP-Atlas
  • SWE-bench Pro
  • Tau2-Bench Airline
  • Tau2-Bench Banking
  • Tau2-Bench Retail
  • Tau2-Bench Telecom
  • Tool-Decathlon
A missing benchmark is not a zero and is never scored as one.

Check it yourself

Every number on this page, with who measured it, the interval they published, the harness it was run under, and where to go and read it. Nothing here was measured by Ferrox Labs.

BenchmarkClassPublishedInterval (normalised) Measured byLicenceHarness / effort
APEX Agents agents 38.5% 31.1 to 46.0 APEX CC-BY-4.0 source
LMArena Agent agents 0.066 35.5 to 41.7 LMArena CC-BY-4.0 source
Terminal-Bench 2 agents 82.2% 77.9 to 86.5 tbench.ai CC-BY-4.0 Codex CLI source
ALE-Bench coding 1589 not published ALE-Bench (Sakana AI with AtCoder) CC-BY-4.0 / medium source
DeepSWE coding 54.0% 51.4 to 56.5 deepswe.datacurve.ai CC-BY-4.0 mini-swe-agent / medium source
FrontierCode coding 43.0% not published cognition.com CC-BY-4.0 codex / xhigh source
LMArena Text (style-controlled) preference 1477 81.8 to 82.9 LMArena CC-BY-4.0 source
LMArena WebDev preference 1458 56.4 to 58.0 LMArena CC-BY-4.0 source
ARC-AGI-2 reasoning 83.3% not published ARC Prize CC-BY-4.0 / high source
GPQA Diamond (Epoch's own run) reasoning 77.3% 71.4 to 83.1 Epoch AI CC-BY-4.0 / none source
OTIS Mock AIME 2024-2025 reasoning 57.8% 43.2 to 72.4 Epoch AI CC-BY-4.0 / none source

Configurations rolled up: 17. Rule: median observed configuration per benchmark (lower median, always a real measurement). Harnesses seen: capy, clnkr, codex, codex-cli, mini-swe-agent, nexau-ahe.

Snapshot 2026-08-30-62e4e85f043b, manifest dc46131315046f1e. Grades are positional: position in the measured field, as a percentile of rank among ranked entries, n=37.