BenchLM’s August 2026 agentic AI leaderboard, refreshed August 7, has Claude Opus 5 sitting at the top of the category with a BenchAlign agentic score of 80.7, ahead of two other Anthropic models: Claude Mythos 5 (75.6) and Claude Fable 5 (75.3). Kimi K3 from Moonshot AI rounds out the top four at 74.1, followed by OpenAI’s GPT-5.6 Sol at 68.1.
If those numbers look tightly clustered near the top, that’s because they are — Opus 5’s lead over Mythos 5 is about five points, and the gap only widens as you move further down the ranked list of 380 tracked models.
What the score actually measures
BenchLM’s agentic category isn’t a single benchmark — it’s an aggregate across 27 tracked evaluations spanning tool use, browser research, and computer-use workflows, including Terminal-Bench 2.0, BrowseComp, OSWorld-Verified, OSWorld 2.0, CyberGym, AndroidWorld, WebVoyager, MCP Atlas, and more. BenchLM describes its methodology as calibrating each evidence source for difficulty before aggregating into a single “BenchAlign” score, and it explicitly separates models into “Supported” (diverse direct evidence) versus “Estimated” (ranked, but with wider uncertainty due to thinner benchmark coverage) categories. As of this refresh, the leaderboard shows 42 Supported and 90 Estimated models out of 380 total.
Terminal-Bench 2.0 — testing agentic software engineering and terminal task completion — carries the heaviest individual weight in the category score at 38%, according to BenchLM’s benchmark breakdown.
The BrowseComp nuance worth flagging
Here’s where the leaderboard gets more interesting than the headline number suggests. On the narrower BrowseComp sub-benchmark specifically — a test of browser-based research capability — Claude Opus 5’s score of 90.8 actually ranks third, not first. Per BenchLM’s own BrowseComp-specific data, GPT-5.6 Sol leads that individual benchmark at 92.2, followed by Kimi K3 at 91.2, with Opus 5 in third place.
This matters because it’s easy to read “Opus 5 leads the agentic category” and assume it’s dominant across every individual sub-benchmark that feeds into that score. It isn’t. Opus 5’s strength is in the aggregate — consistent, strong performance across the full weighted mix of 27 evaluations — rather than in claiming the top spot on every individual test. On Terminal-Bench 2.0 specifically, for instance, GPT-5.6 Sol leads at 91.9, with Kimi K3 second at 88.3 and Claude Mythos 5 (not Opus 5) third at 88.0.
The practical takeaway for anyone choosing a model for a specific agentic workload: if your use case leans heavily on one particular capability — say, pure browser research — checking that individual sub-benchmark score, rather than relying on the aggregate category ranking, will give you a more accurate picture than the headline leaderboard position alone.
Where the rest of the field lands
Below the top five, the ranked list features a mix of closed and open-weight models. MiniMax M3 is called out by BenchLM as the “Best open weight” pick for agentic work at 68.8 overall, while Kimi K3 doubles as the “Best Agentic value” option given its $15-per-million-token output pricing. Grok 4.20 from xAI claims the “Largest useful context window” distinction at 2 million tokens, and NVIDIA’s Nemotron 3 Nano Omni 30B A3B is flagged as the fastest measured model in the category at 324 tokens per second — a reminder that “best” on an aggregate leaderboard and “best fit for your constraints” (cost, speed, context window, open weights) are frequently different questions with different answers.
Further down the ranked list: Claude Sonnet 5 sits at 66.5, Meta’s Muse Spark 1.1 at 65.2, GPT-5.5 at 63.7, and GLM-5.2 — the top-ranked open-weight model with “Supported” evidence status — at 58.2, well below MiniMax M3’s Estimated-status 68.8.
Reading leaderboards like this one
BenchLM is transparent about a limitation worth internalizing whenever you check any aggregate model leaderboard: “Estimated” rows carry wider uncertainty because they have less diverse direct evidence, not because the underlying model is necessarily weaker. A newly released model with thinner benchmark coverage isn’t automatically penalized to the bottom of the list — it’s ranked with the caveat attached instead. That’s a reasonable design choice, but it also means two models sitting close together on the list may have meaningfully different confidence behind their scores.
For teams actually choosing between agentic models for production use, the headline “#1 in agentic” is a useful starting signal, but the sub-benchmark breakdown — and whether your specific workload maps more closely to Terminal-Bench-style coding tasks or BrowseComp-style research tasks — is the detail that should drive the final decision.
Sources
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260808-0800
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