Who ranks #1 on the BrowseComp leaderboard?
As of August 20, 2026, Kimi K3 by Moonshot AI ranks #1 on BrowseComp at 91.2%. API pricing is $3.00/M input and $15.00/M output.
As of August 20, 2026, Kimi K3 is #1 for BrowseComp at 91.2%. Ranked by the BrowseComp score BrowseComp leaderboard for web-research agents, plus API pricing. Rank LLMs on multi-step browsing and retrieval.
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As of August 20, 2026, Kimi K3 by Moonshot AI is #1 for BrowseComp at 91.2%. Ranked by the BrowseComp score This board also tracks BrowseComp, SimpleQA, MCP Atlas. Next on the same board: Claude Opus 5 and GPT-5.6 Sol. Related leaders: DeepSeek-V3.2-Exp on SimpleQA at 97.1%; Muse Spark 1.1 on MCP Atlas at 88.1%. This browsecomp leaderboard ranks models by BrowseComp. Scores come from public evals. Prices are the live API rates in the table above.
Sources: BrowseComp (OpenAI) (https://openai.com/index/browsecomp/); 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 | BrowseComp | Input /M | Output /M |
|---|---|---|---|---|
| 1 | Kimi K3 | 91.2% | $3.00 | $15.00 |
| 2 | Claude Opus 5 | 90.8% | $5.00 | $25.00 |
| 3 | GPT-5.6 Sol | 90.4% | $5.00 | $30.00 |
| 4 | GPT-5.5 Pro | 90.1% | $60.00 | $480.00 |
| 5 | Claude Mythos 5 | 88% | $10.00 | $50.00 |
| 6 | GPT-5.6 Terra | 87.5% | $2.00 | $12.00 |
| 7 | Claude Mythos Preview | 86.9% | $10.00 | $50.00 |
| 8 | Kimi K2.6 | 86.3% | $0.96 | $3.97 |
Rank one eval at a time. All LLM benchmarks.
As of August 20, 2026, Kimi K3 by Moonshot AI ranks #1 on BrowseComp at 91.2%. API pricing is $3.00/M input and $15.00/M output.
The current BrowseComp ranking as of August 20, 2026 is 1. Kimi K3 at 91.2%; 2. Claude Opus 5 at 90.8%; 3. GPT-5.6 Sol at 90.4%.
Nemotron 3.5 Lightning (30B A3B) is the cheapest scored model on this browsecomp leaderboard at $0.05/M input and $0.20/M output ($0.25 blended). Kimi K3 still leads BrowseComp at 91.2%.
Not automatically. Kimi K3 leads BrowseComp, but a cheaper scored model can be the better production choice if the quality gap is small. Use the table to weigh BrowseComp 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.
BrowseComp tests whether a model can search the web, follow sources, and answer hard research questions that need more than one lookup.
No. It ranks the model that is doing the browsing, not Google or Bing. Use it when you are buying an agent that researches on the user's behalf.
SimpleQA checks short-form factuality. A model that browses well should also keep hallucination rates down on easier questions.