THE FORGE RANKINGS·2026-08-30

Claude Opus 4.6

Anthropic
15
RANK of 37 ranked
FERROX INDEX70.8 weighted mean of category positions
GRADEB above the field median
EVIDENCE13 / 32 benchmarks measured
QUALITYTHIN Correlated evidence in: preference. Those category scores rest on benchmarks from a single family.

Not separable from GLM-5.2 and GPT-5.6 Luna 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

9of 37 measured
47.5
13.1median 38.963.0

Coding

29of 37 measured
43.6
22.4median 50.070.7

Reasoning

21of 37 measured
60.1
18.1median 62.893.0

Preference

3of 15 measured
76.2
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

24/35
997 best 2177 · GPT-5.6 Sol
ALE-Bench (Sakana AI with AtCoder) CC-BY-4.0

LMArena Agent

11/21
0.055 best 0.127 · Claude Opus 5
LMArena CC-BY-4.0

LMArena Text (style-controlled)

2/20
1497 best 1507 · Claude Fable 5
LMArena CC-BY-4.0

FrontierCode

14/17
26.6% best 53.5% · Claude Fable 5
cognition.com CC-BY-4.0 claude-code high

LMArena WebDev

5/16
1536 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 32.1% 25.0 to 39.2 APEX CC-BY-4.0 / max source
LMArena Agent agents 0.055 30.9 to 39.4 LMArena CC-BY-4.0 source
Terminal-Bench 2 agents 75.3% 70.6 to 80.0 tbench.ai CC-BY-4.0 Capy source
ALE-Bench coding 997 not published ALE-Bench (Sakana AI with AtCoder) CC-BY-4.0 source
FrontierCode coding 26.6% not published cognition.com CC-BY-4.0 claude-code / high source
SWE-bench Verified (Epoch's own run) coding 75.6% 71.8 to 79.5 Epoch AI CC-BY-4.0 source
LMArena Text (style-controlled) preference 1497 84.8 to 85.8 LMArena CC-BY-4.0 source
LMArena WebDev preference 1536 66.3 to 67.7 LMArena CC-BY-4.0 source
ARC-AGI-2 reasoning 66.3% not published ARC Prize CC-BY-4.0 / 120k source
FrontierMath reasoning 39.7% 34.0 to 45.3 Epoch AI CC-BY-4.0 / 64k source
GPQA Diamond (Epoch's own run) reasoning 88.8% 85.0 to 92.6 Epoch AI CC-BY-4.0 / 64k source
Humanity's Last Exam reasoning 19.0% 16.0 to 22.0 Humanity’s Last Exam (CAIS / Scale AI) CC-BY-4.0 source
OTIS Mock AIME 2024-2025 reasoning 93.1% 86.5 to 99.6 Epoch AI CC-BY-4.0 / 32k source

Configurations rolled up: 11. Rule: median observed configuration per benchmark (lower median, always a real measurement). Harnesses seen: capy, claude-code, droid, forgecode, maya-v2, meta-harness.

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.