Who ranks #1 on the Mega Mlqa leaderboard?
As of August 20, 2026, Phi-3.5-mini-instruct by Microsoft ranks #1 on Mega Mlqa at 61.7%. API pricing is $0.10/M input and $0.10/M output.
As of August 20, 2026, Phi-3.5-mini-instruct is #1 for Mega Mlqa at 61.7%. Ranked by the Mega Mlqa score 1 models in this index have a published Mega Mlqa score. Mega Mlqa leaderboard: rank models by Mega Mlqa next to live API token prices.
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As of August 20, 2026, Phi-3.5-mini-instruct by Microsoft is #1 for Mega Mlqa at 61.7%. Ranked by the Mega Mlqa score This board also tracks Mega Mlqa. This mega mlqa leaderboard ranks models by Mega Mlqa. 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 | Mega Mlqa | Input /M | Output /M |
|---|---|---|---|---|
| 1 | Phi-3.5-mini-instruct | 61.7% | $0.10 | $0.10 |
Rank one eval at a time. All LLM benchmarks.
As of August 20, 2026, Phi-3.5-mini-instruct by Microsoft ranks #1 on Mega Mlqa at 61.7%. API pricing is $0.10/M input and $0.10/M output.
Phi-3.5-mini-instruct currently leads Mega Mlqa 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 Mega Mlqa, but a cheaper scored model can be the better production choice if the quality gap is small. Use the table to weigh Mega Mlqa 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.
Mega Mlqa is a public LLM eval (the Mega Mlqa score). This page ranks models that have published a score, next to live API prices.
This page is the Mega Mlqa leaderboard. Models are sorted by Mega Mlqa, 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 Mega Mlqa score next to live API $/M so you can pick a production SKU, not only a trophy number.