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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.

36 configurationsAgenticDated Mercor configurationsDisplay onlyUpdated November 12, 2026 capture

Mean Score table (36 configurations)

Mean Score results for GeneBench-Pro (Mercor)
RankModel / configurationScoreParameters (B)Open / closed
1Sonnet 5.5AnthropicTerminus 2 · max reasoningPublished margin ±28.5 · 99 reported samples
39.00%
Not reportedUnknown
2GPT-6 AstraOpenAITerminus 2 · max reasoningPublished margin ±23.5 · 100 reported samples
36.00%
Not reportedUnknown
3GPT-6.1 SolOpenAITerminus 2 · max reasoningPublished margin ±23.5 · 100 reported samples
33.00%
Not reportedUnknown
4Opus 5.5AnthropicTerminus 2 · max reasoningPublished margin ±30 · 100 reported samples
30.00%
Not reportedUnknown
5Muse Wonder 1.0MetaTerminus 2 · max reasoningPublished margin ±30 · 20 reported samples
30.00%
Not reportedUnknown
6Muse Spark 1.3MetaTerminus 2 · max reasoningPublished margin ±23.5 · 100 reported samples
26.00%
Not reportedUnknown
7Fable 5.1AnthropicTerminus 2 · max reasoningPublished margin ±26 · 100 reported samples
26.00%
Not reportedUnknown
8GPT-6 SolOpenAITerminus 2 · max reasoningPublished margin ±24 · 100 reported samples
26.00%
Not reportedUnknown
9GPT-5.6 SolOpenAITerminus 2 · max reasoningPublished margin ±19 · 100 reported samples
23.00%
Not reportedUnknown
10Opus 5AnthropicTerminus 2 · max reasoningPublished margin ±21 · 100 reported samples
22.00%
Not reportedUnknown
11GLM-5.3ZhipuTerminus 2 · max reasoningPublished margin ±17.5 · 100 reported samples
15.00%
Not reportedUnknown
12Grok 4.6xAITerminus 2 · xhigh reasoningPublished margin ±18 · 100 reported samples
15.00%
Not reportedUnknown
13Fable 5AnthropicTerminus 2 · max reasoningPublished margin ±17.5 · 100 reported samples
14.00%
Not reportedUnknown
14Gemini 3.7 FlashGoogleTerminus 2 · high reasoningPublished margin ±17 · 100 reported samples
14.00%
Not reportedUnknown
15GPT-6 LunaOpenAITerminus 2 · max reasoningPublished margin ±16 · 100 reported samples
13.00%
Not reportedUnknown
16DeepSeek-V4.1-FlashDeepSeekTerminus 2 · max reasoningPublished margin ±9.5 · 100 reported samples
11.00%
Not reportedUnknown
17Grok 4.5xAITerminus 2 · high reasoningPublished margin ±14.5 · 100 reported samples
11.00%
Not reportedUnknown
18Opus 4.7AnthropicTerminus 2 · max reasoningPublished margin ±15.5 · 100 reported samples
11.00%
Not reportedUnknown
19Gemini 3.8 FlashGoogleTerminus 2 · high reasoningPublished margin ±15.5 · 100 reported samples
11.00%
Not reportedUnknown
20Gemini 3.6 FlashGoogleTerminus 2 · high reasoningPublished margin ±14 · 100 reported samples
10.00%
Not reportedUnknown
21Grok 4.7xAITerminus 2 · xhigh reasoningPublished margin ±15 · 100 reported samples
10.00%
Not reportedUnknown
22Sonnet 5AnthropicTerminus 2 · max reasoningPublished margin ±11 · 100 reported samples
10.00%
Not reportedUnknown
23Muse Spark OpenMetaTerminus 2 · xhigh reasoningPublished margin ±15 · 20 reported samples
10.00%
Not reportedUnknown
24DeepSeek-V4-FlashDeepSeekTerminus 2 · max reasoningPublished margin ±10.5 · 100 reported samples
9.00%
Not reportedUnknown
25Gemini 3.5 FlashGoogleTerminus 2 · high reasoningPublished margin ±12.5 · 100 reported samples
9.00%
Not reportedUnknown
26Kimi K3KimiTerminus 2 · max reasoningPublished margin ±6.5 · 100 reported samples
6.00%
Not reportedUnknown
27GPT-5.6 TerraOpenAITerminus 2 · max reasoningPublished margin ±5.5 · 100 reported samples
5.00%
Not reportedUnknown
28MiniMax-M3MiniMaxTerminus 2 · high reasoningPublished margin ±5 · 100 reported samples
4.00%
Not reportedUnknown
29GPT-5.5OpenAITerminus 2 · xhigh reasoningPublished margin ±6 · 100 reported samples
4.00%
Not reportedUnknown
30BeamReflectionTerminus 2 · max reasoningPublished margin ±4 · 100 reported samples
4.00%
Not reportedUnknown
31GPT-5.6 LunaOpenAITerminus 2 · max reasoningPublished margin ±3.5 · 100 reported samples
3.00%
Not reportedUnknown
32Qwen3.8-MaxAlibabaTerminus 2 · xhigh reasoningPublished margin ±3 · 100 reported samples
2.00%
Not reportedUnknown
33InklingThinking MachinesTerminus 2 · high reasoningPublished margin ±1.5 · 100 reported samples
1.00%
Not reportedUnknown
34Nemotron 3 UltraNVIDIATerminus 2 · high reasoningPublished margin ±1.5 · 100 reported samples
1.00%
Not reportedUnknown
35DeepSeek-V4-Pro-0813DeepSeekTerminus 2 · max reasoningPublished margin ±1.5 · 100 reported samples
1.00%
Not reportedUnknown
36Gemini 3.5 Flash LiteGoogleTerminus 2 · high reasoningPublished margin ±0 · 100 reported samples
0.00%
Not reportedUnknown

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.

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

Dated Mercor configurationsDisplay only

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.

Last updated: November 12, 2026 capture · mirrored from the public benchmark leaderboard

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