Who ranks #1 on the Phibench leaderboard?
As of August 20, 2026, Phi 4 by Microsoft ranks #1 on Phibench at 56.2%. API pricing is $0.07/M input and $0.14/M output.
As of August 20, 2026, Phi 4 is #1 for Phibench at 56.2%. Ranked by the Phibench score 1 models in this index have a published Phibench score. Phibench leaderboard: rank models by Phibench next to live API token prices.
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As of August 20, 2026, Phi 4 by Microsoft is #1 for Phibench at 56.2%. Ranked by the Phibench score This board also tracks Phibench. This phibench leaderboard ranks models by Phibench. Scores come from public evals. Prices are the live API rates in the table above.
Sources: OpenAI API pricing (https://developers.openai.com/api/docs/pricing); Anthropic Claude API pricing (https://platform.claude.com/docs/en/about-claude/pricing); Gemini API pricing (https://ai.google.dev/gemini-api/docs/pricing)
| Rank | Model | Phibench | Input /M | Output /M |
|---|---|---|---|---|
| 1 | Phi 4 | 56.2% | $0.07 | $0.14 |
Rank one eval at a time. All LLM benchmarks.
As of August 20, 2026, Phi 4 by Microsoft ranks #1 on Phibench at 56.2%. API pricing is $0.07/M input and $0.14/M output.
Phi 4 currently leads Phibench and is also the cheapest scored model on this page, at $0.07/M input and $0.14/M output ($0.21 blended).
Not automatically. Phi 4 leads Phibench, but a cheaper scored model can be the better production choice if the quality gap is small. Use the table to weigh Phibench against input/output price, context window, and related evals.
Scores and API prices on this page are refreshed from published evals and provider rates. The snapshot is labeled August 20, 2026. Treat it as a current index, not a one-off blog post.
Phibench is a public LLM eval (the Phibench score). This page ranks models that have published a score, next to live API prices.
This page is the Phibench leaderboard. Models are sorted by Phibench, with input and output token prices on the same row so you can weigh score against cost. Official boards often omit price; that comparison is the point of this index.
Official eval pages own the methodology. This page keeps the published Phibench score next to live API $/M so you can pick a production SKU, not only a trophy number.