THE FORGE RANKINGS·2026-08-30

Claude Opus 4.7

Anthropic
9
RANK of 37 ranked
FERROX INDEX84.0 weighted mean of category positions
GRADEA front rank
EVIDENCE13 / 32 benchmarks measured
QUALITYTHIN Correlated evidence in: preference. Those category scores rest on benchmarks from a single family.

Not separable from GPT-5.4 (2026-03-05) and GLM-5.3 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

5of 37 measured
51.2
13.1median 38.963.0

Coding

14of 37 measured
53.3
22.4median 50.070.7

Reasoning

18of 37 measured
64.0
18.1median 62.893.0

Preference

2of 15 measured
77.3
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.

ALE-Bench

14/35
1323 best 2177 · GPT-5.6 Sol
ALE-Bench (Sakana AI with AtCoder) CC-BY-4.0

APEX Agents

11/28
33.9% best 45.0% · Claude Fable 5
APEX CC-BY-4.0 max

FrontierMath

3/21
43.8% best 47.6% · GPT-5.4 (2026-03-…
Epoch AI CC-BY-4.0 xhigh

SWE-bench Verified (Epoch's own run)

1/21
83.5% best 83.5% · Claude Opus 4.7
Epoch AI CC-BY-4.0 max

LMArena Text (style-controlled)

3/20
1494 best 1507 · Claude Fable 5
LMArena CC-BY-4.0

FrontierCode

12/17
38.5% best 53.5% · Claude Fable 5
cognition.com CC-BY-4.0 claude-code max

Humanity's Last Exam

4/17
36.2% best 46.4% · Gemini 3.1 Pro Pr…
Humanity’s Last Exam (CAIS / Scale AI) CC-BY-4.0

Terminal-Bench 2

3/17
80.2% best 82.2% · GPT-5.5
tbench.ai CC-BY-4.0 WOZCODE

LMArena WebDev

2/16
1558 best 1626 · Claude Fable 5
LMArena CC-BY-4.0

Not measured

19/32
  • DeepSWE
  • 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 33.9% 26.4 to 41.4 APEX CC-BY-4.0 / max source
LMArena Agent agents 0.069 35.1 to 43.9 LMArena CC-BY-4.0 source
Terminal-Bench 2 agents 80.2% 76.1 to 84.3 tbench.ai CC-BY-4.0 WOZCODE source
ALE-Bench coding 1323 not published ALE-Bench (Sakana AI with AtCoder) CC-BY-4.0 source
FrontierCode coding 38.5% not published cognition.com CC-BY-4.0 claude-code / max source
SWE-bench Verified (Epoch's own run) coding 83.5% 80.2 to 86.8 Epoch AI CC-BY-4.0 / max source
LMArena Text (style-controlled) preference 1494 84.3 to 85.5 LMArena CC-BY-4.0 source
LMArena WebDev preference 1558 68.9 to 70.5 LMArena CC-BY-4.0 source
ARC-AGI-2 reasoning 68.3% not published ARC Prize CC-BY-4.0 / high source
FrontierMath reasoning 43.8% 38.1 to 49.5 Epoch AI CC-BY-4.0 / xhigh source
GPQA Diamond (Epoch's own run) reasoning 86.4% 81.6 to 91.2 Epoch AI CC-BY-4.0 / max source
Humanity's Last Exam reasoning 36.2% 32.5 to 39.9 Humanity’s Last Exam (CAIS / Scale AI) CC-BY-4.0 source
OTIS Mock AIME 2024-2025 reasoning 86.7% 76.6 to 96.7 Epoch AI CC-BY-4.0 / max source

Configurations rolled up: 8. Rule: median observed configuration per benchmark (lower median, always a real measurement). Harnesses seen: claude-code, wozcode.

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.