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Benchmark Real-World Intelligence

Evaluate models on code reasoning, vision-language tasks, and agent workflows using verifiable benchmarks built for real-world utility.

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Core Capabilities

Benchmarks designed to stress-test reasoning, perception, and code generation, using real-world tasks and evaluator-calibrated QA.

SWE-bench++

Next-gen benchmark for code reasoning and agent evaluation. Features 7K+ GitHub issues with containerized PRs, log parsing, and trajectory capture for fine-tuning.
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VLM-bench

Open-ended VLM benchmark with 700+ expert-authored prompts spanning business and STEM domains for testing numerical reasoning, spatial inference, and visual QA.
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CodeBench

900+ multilingual coding tasks with deterministic pass/fail scoring. Built for Aider compatibility, regression testing, and QA across Python, Java, Go, Swift, C++, and more.
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Hillclimb + OTS Packs

Off-the-shelf datasets and hillclimb support for public benchmarks like GPQA, HLE, MMMU, MCB, TAU, HealthBench, AIME, and more.
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Why Evaluate Your Model with Turing

Evaluator-Led QA

Our diagnostics use calibrated evaluators and metrics to surface reasoning gaps and ambiguity blind spots.

Benchmarks Grounded in Real Workflows

SWE-bench++, VLM-bench, and RL scenarios mirror production coding and multimodal tasks.

Feedback Structured for Post-Training

Evaluation outputs map to fine-tuning inputs, reward models, and trace-based improvements; ready to plug into your post-training loop.

Diagnostic Briefs

Get concise evaluation summaries that highlight gaps, strengths, and next-step recommendations.

How Our Evaluation Works

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Kickoff & Objective Setting

Align on model goals, datasets, and key performance indicators.

Diagnostic Data Capture

Run structured evaluations, collect performance logs, and gather qualitative feedback loops.

Benchmark Execution

Run curated benchmark suites (e.g., VLM-bench, SWE-bench++) under controlled conditions.

Results & Recommendations

Deliver a diagnostic brief with gap analysis, prioritized improvement paths, and next-step data or pipeline suggestions.

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Run benchmark evaluations like SWE-bench++ and VLM-bench, and get a detailed roadmap for tuning, reward modeling, or data generation.

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From Research to Results

Explore technical contributions and case studies from leading lab partnerships, designed to push reasoning, reward learning, and post-training QA forward.

Case Study

Evaluating Olympiad-Grade Math Reasoning for Salesforce AI Research

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Benchmarking RTL Agents with 1,500+ Real-World Verilog Tasks for NVIDIA’s CVDP

Case Study

Benchmarking RTL Agents with 1,500+ Real-World Verilog Tasks for NVIDIA’s CVDP

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Improving LLM Performance with 4,000+ Apex and SOQL Notebook Tasks

Case Study

Improving LLM Performance with 4,000+ Apex and SOQL Notebook Tasks

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Frequently Asked Questions

What’s included in the diagnostic brief?

A detailed performance report, benchmark comparisons, and prioritized gap analysis with actionable recommendations.

How long does an evaluation take?

From kickoff to brief delivery, typically 1–2 weeks depending on dataset availability and model complexity.

Can I combine evaluation with data generation?

Yes—you can request sample datasets alongside your diagnostics to streamline next-step pipelines.

What happens after the evaluation?

Our team will review findings with you, propose a tailored data-generation plan, and outline a roadmap for optimization.

Want to Know Where Your Model Falls Short?

Validate your model’s strengths and weaknesses before scaling—partner with Turing for a research-driven evaluation.

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