Who ranks #1 on the Qmsum leaderboard?
As of August 31, 2026, Phi-3.5-mini-instruct by Microsoft ranks #1 on Qmsum at 21.3%. API pricing is $0.10/M input and $0.10/M output.
As of August 31, 2026, Phi-3.5-mini-instruct is #1 for Qmsum at 21.3%. Ranked by the Qmsum score 2 models in this index have a published Qmsum score. Qmsum leaderboard: rank models by Qmsum next to live API token prices.
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As of August 31, 2026, Phi-3.5-mini-instruct by Microsoft is #1 for Qmsum at 21.3%. Ranked by the Qmsum score This board also tracks Qmsum. Next on the same board: Phi-3.5-MoE-instruct. This qmsum leaderboard ranks models by Qmsum. 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 | Qmsum | Input /M | Output /M |
|---|---|---|---|---|
| 1 | Phi-3.5-mini-instruct | 21.3% | $0.10 | $0.10 |
| 2 | Phi-3.5-MoE-instruct | 19.9% | $0.07 | $0.14 |
Rank one eval at a time. All LLM benchmarks.
As of August 31, 2026, Phi-3.5-mini-instruct by Microsoft ranks #1 on Qmsum at 21.3%. API pricing is $0.10/M input and $0.10/M output.
The current Qmsum ranking as of August 31, 2026 is 1. Phi-3.5-mini-instruct at 21.3%; 2. Phi-3.5-MoE-instruct at 19.9%.
Phi-3.5-mini-instruct currently leads Qmsum and is also the cheapest scored model on this page, at $0.10/M input and $0.10/M output ($0.20 blended).
Not automatically. Phi-3.5-mini-instruct leads Qmsum, but a cheaper scored model can be the better production choice if the quality gap is small. Use the table to weigh Qmsum 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 31, 2026. Treat it as a current index, not a one-off blog post.
Qmsum is a public LLM eval (the Qmsum score). This page ranks models that have published a score, next to live API prices.
This page is the Qmsum leaderboard. Models are sorted by Qmsum, 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 Qmsum score next to live API $/M so you can pick a production SKU, not only a trophy number.