Top picks for Experiment Design (2026)
Designing rigorous A/B and lab experiments. Ranked from 333 live models on the OpenRouter catalog, weighted for reasoning quality, structured output.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 170 | $3.00 | $15.00 | 1,000,000 | Details → |
| 2 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 170 | $5.00 | $25.00 | 1,000,000 | Details → |
| 3 | OpenAI: GPT-5.4openai/gpt-5.4 | 161 | $2.50 | $15.00 | 1,050,000 | Details → |
| 4 | Z.ai: GLM 5.2z-ai/glm-5.2 | 159 | $0.97 | $3.04 | 1,048,576 | Details → |
| 5 | OpenAI: GPT-5openai/gpt-5 | 159 | $1.25 | $10.00 | 400,000 | Details → |
| 6 | Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 | 158 | $5.00 | $25.00 | 1,000,000 | Details → |
| 7 | OpenAI: GPT-5.6 Terraopenai/gpt-5.6-terra | 158 | $2.50 | $15.00 | 1,050,000 | Details → |
| 8 | Anthropic: Claude Sonnet 5anthropic/claude-sonnet-5 | 157 | $2.00 | $10.00 | 1,000,000 | Details → |
| 9 | xAI: Grok 4.5x-ai/grok-4.5 | 157 | $2.00 | $6.00 | 500,000 | Details → |
| 10 | DeepSeek: DeepSeek V4 Prodeepseek/deepseek-v4-pro | 157 | $0.43 | $0.87 | 1,048,576 | Details → |
| 11 | MoonshotAI: Kimi K2.6moonshotai/kimi-k2.6 | 157 | $0.68 | $3.42 | 262,144 | Details → |
| 12 | OpenAI: GPT-5.6 Lunaopenai/gpt-5.6-luna | 156 | $1.00 | $6.00 | 1,050,000 | Details → |
| 13 | Google: Gemini 3.1 Pro Previewgoogle/gemini-3.1-pro-preview | 155 | $2.00 | $12.00 | 1,048,576 | Details → |
| 14 | Google: Gemini 3.5 Flashgoogle/gemini-3.5-flash | 155 | $1.50 | $9.00 | 1,048,576 | Details → |
| 15 | OpenAI: GPT-5.5openai/gpt-5.5 | 155 | $5.00 | $30.00 | 1,050,000 | Details → |
How we ranked these
For Experiment Design, we weight models on reasoning quality, structured output. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About Experiment Design
Experiment design is the process of structuring A/B tests and lab experiments to produce statistically valid, actionable results. You need this task when you're planning controlled tests for product features, marketing campaigns, or scientific hypotheses, and you want to avoid false positives and wasted resources. Good models excel at identifying confounding variables, calculating required sample sizes, and spotting flawed randomization schemes. They catch things like survivorship bias in your control group or misaligned traffic splits that would invalidate results. Poor models generate generic templates and miss domain-specific pitfalls. The main speed tradeoff: a thorough design takes 15-30 minutes with model assistance, but saves weeks of wasted experiment time downstream.
When to use: Use this when you're building a new A/B test, lab study, or feature rollout and want to make sure your design will actually answer the question you're asking before you run it.
Common questions
What is the difference between a properly designed experiment and one that will give you false results?
A proper design specifies sample size upfront (using power analysis), randomizes assignment correctly, minimizes confounders, and defines success metrics before the test runs. Weak designs skip power analysis, let traffic split unevenly, or switch metrics mid-run based on interim results. Claude and GPT-4 can catch most of these issues if you describe your setup clearly.
How long does a model actually take to help design an experiment versus building it yourself?
Most models generate a solid first draft (hypothesis, sample size, randomization plan, success thresholds) in 2-5 minutes. Manual review and refinement typically adds 10-20 minutes. Building from scratch without a model takes 45 minutes to an hour for someone experienced, longer if you're new to experiment design.