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RedlineBench — Mercor evaluation (RedlineBench (Mercor))

We show this table for reference; we do not rank on it.

Negotiating and revising contracts across multiple turns. 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.

Turn-Weighted Mean Score on RedlineBench (Mercor) — October 15, 2026 capture

We mirror the published turn-weighted mean score view for RedlineBench (Mercor). GPT-5.6 Sol leads the public snapshot at 46.80%, followed by GPT-5.5 (44.10%) and GPT-5.6 Terra (44.00%). We do not use these results to rank models overall.

12 configurationsAgenticDated Mercor configurationsDisplay onlyUpdated October 15, 2026 capture

Turn-Weighted Mean Score table (12 configurations)

Turn-Weighted Mean Score results for RedlineBench (Mercor)
RankModel / configurationScoreParameters (B)Open / closed
1GPT-5.6 SolOpenAIRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · max reasoningMargin not reported · 420 reported samples
46.80%
Not reportedUnknown
2GPT-5.5OpenAIRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · xhigh reasoningMargin not reported · 420 reported samples
44.10%
Not reportedUnknown
3GPT-5.6 TerraOpenAIRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · max reasoningMargin not reported · 420 reported samples
44.00%
Not reportedUnknown
4Opus 5AnthropicRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · max reasoningMargin not reported · 420 reported samples
44.00%
Not reportedUnknown
5GPT-5.6 LunaOpenAIRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · max reasoningMargin not reported · 420 reported samples
43.30%
Not reportedUnknown
6Fable 5AnthropicRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · max reasoningMargin not reported · 420 reported samples
42.40%
Not reportedUnknown
7Gemini 3.6 FlashGoogleRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · high reasoningMargin not reported · 420 reported samples
41.60%
Not reportedUnknown
8GLM-5.2ZhipuRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · max reasoningMargin not reported · 420 reported samples
39.60%
Not reportedUnknown
9Haiku 5.5AnthropicRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · max reasoningMargin not reported · 420 reported samples
39.60%
Not reportedUnknown
10Kimi K3KimiRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · max reasoningMargin not reported · 420 reported samples
39.40%
Not reportedUnknown
11Gemini 3.5 FlashGoogleRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · high reasoningMargin not reported · 420 reported samples
39.10%
Not reportedUnknown
12Opus 4.8AnthropicRedlineBench Harbor — Claude Code (gen-only, disable_verif+capture) · max reasoningMargin not reported · 420 reported samples
38.20%
Not reportedUnknown

How to read this Mercor evaluation

We captured Mercor's RedlineBench results on October 15, 2026. The table preserves 12 published configurations, their source identifiers, reasoning settings, and disclosed turn-weighted 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.

The score is a turn-weighted mean, not task pass rate. Mercor uses a uniform Claude Code Harbor setup and a three-model judge panel; sample-task grades come from one judge and are not the leaderboard score. The panel replaces the original Gemini 3.1 judge with Gemini 3.5 Flash.

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 expert-authored rubrics, scored criterion by criterion and a judge of 3-LLM judge panel over expert rubrics. Its run-count label is k = 3. The linked result page takes precedence where its setup differs, as noted above.

The published RedlineBench (Mercor) snapshot places GPT-5.6 Sol first at 46.80%. The third row is 2.80 points behind. The broader top-10 range is 7.40 points, so many of the published results sit in a relatively narrow band.

12 configurations are shown for RedlineBench (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. RedlineBench (Mercor) is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.

About RedlineBench (Mercor)

Tasks

140 source-reported tasks

Format

Turn-Weighted Mean Score

Difficulty

Source-specific evaluation

The selected headline is Turn-Weighted Mean Score. Source model identifiers, effort settings, published margins, and reported sample counts remain attached to the source configuration. Public-set results and Mercor task extensions occupy separate tables.

Freshness and provenance

Version

RedlineBench; Mercor Turn-Weighted 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 RedlineBench (Mercor) measure?

Negotiating and revising contracts across multiple turns. This table shows Mercor-run configurations for reference and is excluded from model rankings.

Which model leads the published RedlineBench (Mercor) snapshot?

GPT-5.6 Sol currently leads the published RedlineBench (Mercor) snapshot with 46.80% turn-weighted mean score. BenchLM shows this benchmark for display only and does not use it in overall rankings.

How many models are evaluated on RedlineBench (Mercor)?

The October 15, 2026 capture snapshot contains 12 source configurations.

Last updated: October 15, 2026 capture · mirrored from the public benchmark leaderboard

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