Select models to compare
GPT-5.5
OpenAI
Claude Opus 4
Anthropic
Gemini 2.5 Pro
Google
GPT-4o
OpenAI
Claude Sonnet 4
Anthropic
Llama 4 Scout
Meta
Benchmark scores
| Benchmark | GPT-5.5 | Claude Opus 4 | Gemini 2.5 Pro | GPT-4o | Claude Sonnet 4 | Llama 4 Scout |
|---|
Model overview
GPT-5.5
OpenAI
Claude Opus 4
Anthropic
Gemini 2.5 Pro
Google
API pricing (per 1M tokens)
| Model | Input | Output | Context window | Best for |
|---|---|---|---|---|
| GPT-5.5 | $2.00 | $8.00 | 128K | Coding, agents |
| Claude Opus 4 | $15.00 | $75.00 | 200K | Long context, reasoning |
| Gemini 2.5 Pro | $1.25 | $5.00 | 1M | Multimodal, large docs |
| GPT-4o | $2.50 | $10.00 | 128K | Balanced performance |
| Claude Sonnet 4 | $3.00 | $15.00 | 200K | Cost-efficient quality |
| Llama 4 Scout | $0.11 | $0.34 | 10M | Open source, budget |
About this benchmark comparator
This tool aggregates benchmark results from official model evaluations and independent testing to give developers a clear picture of how leading AI models compare. Benchmarks include HumanEval and SWE-bench for coding, MMLU and GPQA for knowledge, MATH and AIME for mathematics, and HELM for overall capability.
Benchmark scores should be treated as directional signals rather than definitive rankings. Real-world performance varies significantly by task type, prompt style, and use case. The best model for your project depends on your specific requirements for capability, cost, context length, and latency.
Frequently asked questions
Which AI model is best for coding?
GPT-5.5 leads on HumanEval and SWE-bench coding benchmarks as of mid-2026. Claude Opus 4 and Claude Sonnet 4 are strong alternatives particularly for longer codebases due to their 200K context window. For open-source use cases, Llama 4 is competitive at a fraction of the cost.
Which model has the largest context window?
Llama 4 Scout supports a 10 million token context window — by far the largest available. Gemini 2.5 Pro supports 1 million tokens. Claude models support 200K. GPT models support 128K.
What is MMLU?
MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects including mathematics, history, law, medicine, and computer science. It is one of the most widely used benchmarks for measuring broad knowledge.
What is HumanEval?
HumanEval is a coding benchmark from OpenAI consisting of 164 Python programming problems. Models must generate code that passes unit tests. It measures practical coding ability rather than theoretical knowledge.