APEX Benchmarks
The AI Productivity Index family of benchmarks assesses whether frontier AI models can perform economically valuable tasks across professional services, medicine, and software engineering.
Our benchmarks
Each benchmark tests a different dimension of professional capability. All tasks are built with Mercor experts and leading industry partners.
APEX-Agents
Long-horizon, cross-application tasks in professional services
Tests whether AI agents can complete multi-hour professional tasks across investment banking, corporate law, and management consulting, using real tools in Google Suite.
Opus 5Max
60.6% ±3.6%
Fable 5Max
59.2% ±3.7%
Muse Spark 1.1xHigh
58.1% ±3.4%
APEX-Accounting
Long-horizon, cross-application tasks in professional accounting
Measuring AI agents ability to complete professional accounting tasks across tools like accounting software, spreadsheets, and PDFs.
Fable 5Max
56.4% ±3.8%
Muse Spark 1.1xHigh
52.6% ±3.7%
GPT-5.6 SolMax
51.5% ±3.9%
APEX-SWE
Real-world software engineering across integration and observability
Measures AI performance on real-world software engineering tasks, from bug fixes to feature builds. Built in collaboration with Cognition.
Opus 5Max
63.7% ±6.4%
Fable 5Max
58.8% ±6.4%
Grok 4.6High
56.4% ±6.2%
APEX-1
Single-turn text tasks
Tests whether frontier models can perform economically valuable tasks across professional domains including investment banking, corporate law, management consulting, and medicine.
GPT-5.6 TerraMax
69.5% ±2.4%
Opus 5xHigh
68.6% ±2.9%
Kimi K3Max
68.4% ±2.3%
Mercor-extended benchmarks
Open-source benchmarks extended with new expert-built Mercor tasks.
Long Context Reasoning (AA-LCR) Extended
Long Context Reasoning (AA-LCR): 100 hard questions over 234 documents in 30 sets (~100k tokens/question) that require synthesizing information across multiple documents and complex reasoning rather than simple retrieval.
GPT-5.6 TerraMax
Score on the original open-source benchmark: 81.8% / Score on this Mercor-extended benchmark: 74.4%
Opus 5Max
Score on the original open-source benchmark: 80.5% / Score on this Mercor-extended benchmark: 73.9%
GPT-5.6 LunaMax
Score on the original open-source benchmark: 80.7% / Score on this Mercor-extended benchmark: 72.2%
SWE Atlas - Codebase QnA Extended
Benchmarks coding agents beyond issue resolution across Codebase Q&A (124 tasks), test writing (90), and refactoring (70), combining programmatic validation with rubric-based software-quality scoring (maintainability, abstractions, hygiene). Uses under-specified, agentic task formulations.
Opus 5Max
Score on the original open-source benchmark: 50.3% / Score on this Mercor-extended benchmark: 40.0%
Sonnet 5Max
Score on the original open-source benchmark: 39.5% / Score on this Mercor-extended benchmark: 32.7%
Kimi K3Max
Score on the original open-source benchmark: 52.7% / Score on this Mercor-extended benchmark: 24.0%
SWE-bench Verified Extended
Tests whether LLMs can resolve real-world GitHub issues by editing codebases, spanning 2,294 problems from 12 popular Python repos and requiring multi-file, long-context changes.
Opus 5Max
Score on the original open-source benchmark: 93.5% / Score on this Mercor-extended benchmark: 82.0%
Fable 5Max
Score on the original open-source benchmark: 95.9% / Score on this Mercor-extended benchmark: 78.7%
Grok 4.5High
Score on the original open-source benchmark: 81.7% / Score on this Mercor-extended benchmark: 70.7%
Terminal-Bench 2.1 Extended
Evaluates AI agents on hard, realistic long-horizon command-line tasks — 89 curated tasks with unique environments, human-written solutions, and verification tests.
GPT-5.5xHigh
Score on the original open-source benchmark: 83.1% / Score on this Mercor-extended benchmark: 38.0%
Gemini 3.1 ProHigh
Score on the original open-source benchmark: 72.7% / Score on this Mercor-extended benchmark: 35.0%
Opus 5High
Score on the original open-source benchmark: 83.9% / Score on this Mercor-extended benchmark: 34.0%
Open-source benchmarks
Popular open-source benchmarks, independently evaluated by Mercor.
SWE Atlas - Codebase QnA
Benchmarks coding agents beyond issue resolution across Codebase Q&A (124 tasks), test writing (90), and refactoring (70), combining programmatic validation with rubric-based software-quality scoring (maintainability, abstractions, hygiene). Uses under-specified, agentic task formulations.
Kimi K3Max
52.7%
Opus 5Max
50.3%
Opus 4.7Max
47.0%
Long Context Reasoning (AA-LCR)
Long Context Reasoning (AA-LCR): 100 hard questions over 234 documents in 30 sets (~100k tokens/question) that require synthesizing information across multiple documents and complex reasoning rather than simple retrieval.
GPT-5.5xHigh
82.8%
GPT-5.4xHigh
82.5%
GPT-5.6 TerraMax
81.8%
MMLU-Pro
An enhanced MMLU adding harder, reasoning-focused questions and expanding choices from 4 to 10 options. The MMLU benchmark evaluates an AI model's general knowledge and reasoning skills using multiple-choice questions across 57 academic and professional subjects.
Opus 5Max
92.0%
Gemini 3.1 ProHigh
91.3%
Opus 4.8Max
90.0%
MMMU-Pro
A more robust multimodal benchmark that filters text-only-solvable questions, expands answer options, and adds vision-only inputs (text embedded in images) to test true joint visual+textual reasoning. Forces models to 'see' and 'read' simultaneously.
Gemini 3.7 FlashHigh
85.2%
Gemini 3.5 FlashHigh
84.7%
Gemini 3.1 ProHigh
84.3%
AdvancedIF
Evaluates complex, multi-turn, and system-level instruction following via expert-curated rubrics over 1,600+ prompts. Paired with a reinforcement-learning method for improving instruction following.
Gemini 3.1 ProHigh
86.7%
Gemini 3.6 FlashHigh
85.3%
GPT-5.6 SolMax • Pro
82.1%
CharXiv
Evaluates multimodal models on realistic chart understanding using 2,323 hand-curated arXiv charts, with descriptive questions (basic elements) and reasoning questions (synthesis across complex elements).
Gemini 3.7 FlashHigh
95.3%
Gemini 3.6 FlashHigh
95.1%
Opus 5Max
94.6%
DeepResearch Bench II
A bilingual benchmark of 130 open-ended research briefs across 22 domains, scored against 9,287 expert-written criteria. Each task is derived from a real review article that the model is explicitly forbidden from consulting, and credit only comes from independently rediscovering the findings.
Opus 5High
56.1%
Sonnet 4.6High
53.3%
GPT 5.6 SolMedium
51.5%
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