GeneBench-Pro — Mercor evaluation (GeneBench-Pro (Mercor))
We show this table for reference; we do not rank on it.
Taking messy genomics datasets through quality control, modeling and diagnostics to a verifiable answer. This table shows Mercor-run configurations for reference and is excluded from model rankings.
Evaluation results published by Mercor. Published evaluation aggregates only; task contents and dataset license grants are not included.
Mean Score on GeneBench-Pro (Mercor) — November 12, 2026 capture
We mirror the published mean score view for GeneBench-Pro (Mercor). Sonnet 5.5 leads the public snapshot at 39.00%, followed by GPT-6 Astra (36.00%) and GPT-6.1 Sol (33.00%). We do not use these results to rank models overall.
Sonnet 5.5
Anthropic
Terminus 2 · max reasoning
Published margin ±28.5 · 99 reported samples
GPT-6 Astra
OpenAI
Terminus 2 · max reasoning
Published margin ±23.5 · 100 reported samples
GPT-6.1 Sol
OpenAI
Terminus 2 · max reasoning
Published margin ±23.5 · 100 reported samples
36 configurationsAgenticDated Mercor configurationsDisplay onlyUpdated November 12, 2026 capture
Mean Score table (36 configurations)
| Rank | Model / configuration | Score | Parameters (B) | Open / closed |
|---|---|---|---|---|
| 1 | Sonnet 5.5AnthropicTerminus 2 · max reasoningPublished margin ±28.5 · 99 reported samples | 39.00% | Not reported | Unknown |
| 2 | GPT-6 AstraOpenAITerminus 2 · max reasoningPublished margin ±23.5 · 100 reported samples | 36.00% | Not reported | Unknown |
| 3 | GPT-6.1 SolOpenAITerminus 2 · max reasoningPublished margin ±23.5 · 100 reported samples | 33.00% | Not reported | Unknown |
| 4 | Opus 5.5AnthropicTerminus 2 · max reasoningPublished margin ±30 · 100 reported samples | 30.00% | Not reported | Unknown |
| 5 | Muse Wonder 1.0MetaTerminus 2 · max reasoningPublished margin ±30 · 20 reported samples | 30.00% | Not reported | Unknown |
| 6 | Muse Spark 1.3MetaTerminus 2 · max reasoningPublished margin ±23.5 · 100 reported samples | 26.00% | Not reported | Unknown |
| 7 | Fable 5.1AnthropicTerminus 2 · max reasoningPublished margin ±26 · 100 reported samples | 26.00% | Not reported | Unknown |
| 8 | GPT-6 SolOpenAITerminus 2 · max reasoningPublished margin ±24 · 100 reported samples | 26.00% | Not reported | Unknown |
| 9 | GPT-5.6 SolOpenAITerminus 2 · max reasoningPublished margin ±19 · 100 reported samples | 23.00% | Not reported | Unknown |
| 10 | Opus 5AnthropicTerminus 2 · max reasoningPublished margin ±21 · 100 reported samples | 22.00% | Not reported | Unknown |
| 11 | GLM-5.3ZhipuTerminus 2 · max reasoningPublished margin ±17.5 · 100 reported samples | 15.00% | Not reported | Unknown |
| 12 | Grok 4.6xAITerminus 2 · xhigh reasoningPublished margin ±18 · 100 reported samples | 15.00% | Not reported | Unknown |
| 13 | Fable 5AnthropicTerminus 2 · max reasoningPublished margin ±17.5 · 100 reported samples | 14.00% | Not reported | Unknown |
| 14 | Gemini 3.7 FlashGoogleTerminus 2 · high reasoningPublished margin ±17 · 100 reported samples | 14.00% | Not reported | Unknown |
| 15 | GPT-6 LunaOpenAITerminus 2 · max reasoningPublished margin ±16 · 100 reported samples | 13.00% | Not reported | Unknown |
| 16 | DeepSeek-V4.1-FlashDeepSeekTerminus 2 · max reasoningPublished margin ±9.5 · 100 reported samples | 11.00% | Not reported | Unknown |
| 17 | Grok 4.5xAITerminus 2 · high reasoningPublished margin ±14.5 · 100 reported samples | 11.00% | Not reported | Unknown |
| 18 | Opus 4.7AnthropicTerminus 2 · max reasoningPublished margin ±15.5 · 100 reported samples | 11.00% | Not reported | Unknown |
| 19 | Gemini 3.8 FlashGoogleTerminus 2 · high reasoningPublished margin ±15.5 · 100 reported samples | 11.00% | Not reported | Unknown |
| 20 | Gemini 3.6 FlashGoogleTerminus 2 · high reasoningPublished margin ±14 · 100 reported samples | 10.00% | Not reported | Unknown |
| 21 | Grok 4.7xAITerminus 2 · xhigh reasoningPublished margin ±15 · 100 reported samples | 10.00% | Not reported | Unknown |
| 22 | Sonnet 5AnthropicTerminus 2 · max reasoningPublished margin ±11 · 100 reported samples | 10.00% | Not reported | Unknown |
| 23 | Muse Spark OpenMetaTerminus 2 · xhigh reasoningPublished margin ±15 · 20 reported samples | 10.00% | Not reported | Unknown |
| 24 | DeepSeek-V4-FlashDeepSeekTerminus 2 · max reasoningPublished margin ±10.5 · 100 reported samples | 9.00% | Not reported | Unknown |
| 25 | Gemini 3.5 FlashGoogleTerminus 2 · high reasoningPublished margin ±12.5 · 100 reported samples | 9.00% | Not reported | Unknown |
| 26 | Kimi K3KimiTerminus 2 · max reasoningPublished margin ±6.5 · 100 reported samples | 6.00% | Not reported | Unknown |
| 27 | GPT-5.6 TerraOpenAITerminus 2 · max reasoningPublished margin ±5.5 · 100 reported samples | 5.00% | Not reported | Unknown |
| 28 | MiniMax-M3MiniMaxTerminus 2 · high reasoningPublished margin ±5 · 100 reported samples | 4.00% | Not reported | Unknown |
| 29 | GPT-5.5OpenAITerminus 2 · xhigh reasoningPublished margin ±6 · 100 reported samples | 4.00% | Not reported | Unknown |
| 30 | BeamReflectionTerminus 2 · max reasoningPublished margin ±4 · 100 reported samples | 4.00% | Not reported | Unknown |
| 31 | GPT-5.6 LunaOpenAITerminus 2 · max reasoningPublished margin ±3.5 · 100 reported samples | 3.00% | Not reported | Unknown |
| 32 | Qwen3.8-MaxAlibabaTerminus 2 · xhigh reasoningPublished margin ±3 · 100 reported samples | 2.00% | Not reported | Unknown |
| 33 | InklingThinking MachinesTerminus 2 · high reasoningPublished margin ±1.5 · 100 reported samples | 1.00% | Not reported | Unknown |
| 34 | Nemotron 3 UltraNVIDIATerminus 2 · high reasoningPublished margin ±1.5 · 100 reported samples | 1.00% | Not reported | Unknown |
| 35 | DeepSeek-V4-Pro-0813DeepSeekTerminus 2 · max reasoningPublished margin ±1.5 · 100 reported samples | 1.00% | Not reported | Unknown |
| 36 | Gemini 3.5 Flash LiteGoogleTerminus 2 · high reasoningPublished margin ±0 · 100 reported samples | 0.00% | Not reported | Unknown |
How to read this Mercor evaluation
We captured Mercor's GeneBench-Pro results on November 12, 2026. The table preserves 36 published configurations, their source identifiers, reasoning settings, and disclosed mean score values. These are results from Mercor's evaluation setup; we did not rerun them.
This dated source table is display only and does not enter overall or category rankings. Source configurations retain their external identity, including Pro descriptors and different effort settings.
Mercor scores the 10-problem public set of OpenAI's GeneBench-Pro, with ten runs per model on each problem, so margins are wide. The scores labs report on their own runs of the benchmark are listed separately and are not combined with this table.
The row labels retain published error margins and sample counts when supplied. Missing margins and counts remain unreported; a sample count is not used to reconstruct a task denominator. Except where the dedicated source defines an interval, the confidence level is unreported. Equal displayed scores and overlapping margins do not establish statistical ties. Capture time does not establish individual evaluation dates.
The general methodology lists binary: the final answer is checked against a recoverable target within calibrated tolerances and a judge of Programatic answer checking. Its run-count label is k = 10. The linked result page takes precedence where its setup differs, as noted above.
Snapshot
The published GeneBench-Pro (Mercor) snapshot places Sonnet 5.5 first at 39.00%. The third row is 6.00 points behind. The broader top-10 range is 17.00 points, so the table still separates the published systems.
36 configurations are shown for GeneBench-Pro (Mercor). The benchmark falls in the Agentic category. We keep external benchmark mirrors separate from the weighted global scoring system, so these results remain source-specific evidence. GeneBench-Pro (Mercor) is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
About GeneBench-Pro (Mercor)
Tasks
10 public problems
Format
Mean Score
Difficulty
Source-specific evaluation
Mercor runs the 10-problem public set of OpenAI's GeneBench-Pro, ten times per model on each problem. The headline is the mean score. Scores the labs report on the full benchmark stay on the GeneBench-Pro page.
Freshness and provenance
Version
GeneBench-Pro; Mercor Mean Score
Refresh cadence
Reviewed dated source capture
Staleness state
Dated Mercor configurations
Question availability
Upstream task access and terms; only aggregate results mirrored
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
Questions
What does GeneBench-Pro (Mercor) measure?
Taking messy genomics datasets through quality control, modeling and diagnostics to a verifiable answer. This table shows Mercor-run configurations for reference and is excluded from model rankings.
Which model leads the published GeneBench-Pro (Mercor) snapshot?
Sonnet 5.5 currently leads the published GeneBench-Pro (Mercor) snapshot with 39.00% mean score. BenchLM shows this benchmark for display only and does not use it in overall rankings.
How many models are evaluated on GeneBench-Pro (Mercor)?
The November 12, 2026 capture snapshot contains 36 source configurations.
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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