Provider analysis
LLM API provider benchmark comparison: which provider leads in 2026?
Benchmark scores help you compare model capability across providers, but they tell different stories depending on the workload. This guide compares benchmark coverage and top scores across OpenAI, Anthropic, Google, Mistral, and DeepSeek. For pricing comparisons, see the provider pricing comparison.
Pricing data sourced from our catalog. Check data sources for provenance and freshness.
Coverage
Benchmark coverage by provider
Coverage shows how many models from each provider have benchmark data in the database. Higher coverage means more models can be compared on capability.
| Provider | Total models | With benchmarks | Coverage |
|---|---|---|---|
| OpenAI | 219 | 78 | 36% |
| Anthropic | 24 | 17 | 71% |
| 73 | 20 | 27% | |
| Mistral | 58 | 21 | 36% |
| DeepSeek | 12 | 4 | 33% |
Key benchmarks
What each benchmark measures
MMLU
Massive Multitask Language Understanding — tests knowledge across 57 academic subjects including STEM, humanities, and social sciences.
GPQA Diamond
Graduate-level Google-Proof Q&A — PhD-level science questions in biology, chemistry, and physics designed to be unsearchable.
HumanEval
Code generation from docstrings — tests ability to generate correct Python functions from natural language descriptions.
MATH
Competition-level mathematics — tests ability to solve complex mathematical problems requiring multi-step reasoning.
SWE-bench Verified
Real-world software engineering — tests ability to fix actual GitHub issues in production codebases.
MMMU
Multitask Multimodal Understanding — tests understanding across text, images, audio, and video.
Provider strengths
Provider benchmark strengths
OpenAI
- Strong across all benchmarks
- o3 leads on MATH (97.8%)
- o4-mini leads on HumanEval (97.3%)
- GPT-5.4 leads on GPQA Diamond (92.0%)
Anthropic
- Strong reasoning capabilities
- Claude Opus 4.8 leads on GPQA Diamond (91.3%)
- High scores on SWE-bench Verified
- Strong coding performance
- Strong multimodal capabilities
- Gemini 2.5 Pro leads on MMLU (89.2%)
- High scores on MMMU (82.0%)
- Competitive pricing
Mistral
- Strong open-source models
- Mistral Small 4 leads on GPQA Diamond for open models (71.2%)
- Codestral competitive on coding benchmarks
- Apache 2.0 license
DeepSeek
- Strong reasoning capabilities
- DeepSeek R1 leads on MATH (97.3%)
- Competitive on GPQA Diamond (71.5%)
- MIT license, fully open-source
Tools
Compare models yourself
Related guides
Continue reading
Cross-provider pricing comparison
How pricing compares across OpenAI, Anthropic, Google, Mistral, and DeepSeek.
Model routing cascade
When to use budget vs premium models across providers.
Hidden costs of LLM APIs
Rate limits, latency, evaluation overhead, and vendor risk beyond per-token pricing.
Cache and batch pricing guide
How cached input and batch pricing change cost estimates.
Disclaimer
Benchmark scores are sourced from official provider documentation and the site's model catalog. Different evaluation methods (0-shot vs 5-shot, different prompting) produce different results. This article is not affiliated with any provider.