AI Model Capability Comparison
ML engineering
Only models released within the past year are shown. Only benchmarks covering at least 3 of these models are shown. Unknown release dates are excluded; historical data is retained.
— means no published score for this candidate in this source, not zero.
| No. | Model | 4/11 recent-year models have scores |
|---|---|---|
| 1 |  Claude Opus 5.5 | 49.3OpenCode, 10h, H100Claude Opus 5.5 |
| 2 |  GPT-6 Astra | 44.3OpenCode, 10h, H100GPT-6 Astra |
| 3 | GPT-6.1 Sol | — |
| 4 | Claude Fable 5.1 | 40.2OpenCode, 10h, H100Claude Fable 5.1 |
| 5 |  Gemini 4 Argon | 45.3OpenCode, 10h, H100Gemini 4 Argon |
| 6 | Claude Sonnet 5.5 | — |
| 7 | Claude Opus 5 | — |
| 8 | GPT-6 Sol | — |
| 9 | GPT-6 Luna | — |
| 10 | Claude Haiku 5.5 | — |
| 11 | Claude Mythos Preview | — |
Google DeepMind (model card)
Source: Google DeepMind (model card) ↗
Google model card: publisher results and republished third-party scores, as of October 2026. See methodology for evaluation settings; these are not all independent evaluations. Evaluation methodology ↗
PostTrainBench v1.1 · Source date unknown
PandaNpc Updated Oct 21, 2026, 06:01 UTC
Source: Epoch AI, CC BY 4.0 · Vals.ai · Artificial Analysis (artificialanalysis.ai)
Column guide
- No.
- Display order only. Sorting changes this number; it is not a published capability rank.
- Model
- The evaluated model name and its provider icon. A dash in a score column means no published score is available here, not a zero.
- PostTrainBench v1.1Post-training benchmark v1.1
- Tests whether an agent can improve a base model through post-training under a compute budget. The score summarizes the resulting models on downstream evaluations. Evaluation methodology ↗
Read scores within the same benchmark, source and version. Higher values indicate stronger performance on the reported metric, but different columns do not share a common scale.