Best LLM for OpenClaw
Find the best model for OpenClaw based on agentic capability, orchestration quality, cost-effectiveness, and community benchmarks.
OpenClaw is an open-source agent framework that lets developers build autonomous AI agents capable of using tools, browsing the web, writing code, and completing multi-step tasks. You bring your own API key, and OpenClaw's provider-agnostic architecture works with any model, so choosing the right LLM is critical for both agent reliability and your monthly bill.
Agentic workloads are fundamentally different from single-turn chat. An autonomous agent loops for dozens of steps, re-reads its own scratchpad, and can easily burn hundreds of thousands of tokens per run, so cost-per-task and long-context reliability tend to matter more than raw leaderboard position. The good news as of July 2026: several frontier-adjacent models now offer a full 1M-token context window at flat pricing with no long-context surcharge, and the budget tier has become genuinely capable.
Our rankings are informed by SWE-bench Verified and SWE-bench Pro scores, Terminal-Bench 2.1 results, Artificial Analysis data, and developer community feedback on real-world agent reliability. We weighted orchestration reliability, long-context behavior, and cost-per-task heavily, since these dominate the experience of running OpenClaw agents day to day.
Top Models for OpenClaw in 2026
K3 tops this page because OpenClaw work is terminal work, and K3 posts the highest measured Terminal-Bench 2.1 score here at 85.0, with a flat-priced 1M context and open weights. At $3/$15 that is frontier capability at mid-tier pricing. One thing the ranking cannot see: K3 is verbose, roughly 2x the average output on Artificial Analysis' suite, and it always reasons. Since every figure on this page is priced per token rather than per task, a verbose model's real cost runs above what its rate implies. Budget for that, or run Sonnet 5 below it if predictable spend matters more than peak capability.
Launched June 30, Sonnet 5 is arguably the sweet spot for OpenClaw: 85.2 on SWE-bench Verified and 80.4 on Terminal-Bench 2.1, a full 1M context at flat pricing with no long-context surcharge, and introductory pricing of $2/$10 through August 31 before the standard $3/$15 kicks in. For most agent workloads it delivers near-flagship reliability at a fraction of flagship cost.
MIT-licensed, coding-first, and built with agent frameworks in mind, GLM-5.2 pairs a 1M-token context with open weights, so you can self-host when you outgrow the API. Worth knowing where its number comes from: Zhipu self-reports 81.0 on Terminal-Bench 2.1, while Artificial Analysis measures 77.9. This page uses the independent figure, which is why the score here is lower than the one in the launch materials. Watch the 3x peak surcharge during 14:00-18:00 Beijing time.
Anthropic's flagship leads SWE-bench Verified at 88.6 and remains the most dependable orchestrator we have seen for long, error-prone agent runs. The 1M context is billed flat, cache hits drop input to $0.50/1M, and a Fast mode tier ($10/$50) is available when latency matters. Reserve it for the tasks where a failed run costs more than the tokens.
At $0.14/$0.28 per 1M tokens with a 1M context window and near-free cache hits, V4-Flash makes long agent loops almost too cheap to meter. Its three reasoning modes let you dial effort per step. One caveat: from the mid-July GA, rates double during Beijing peak hours, so cost-sensitive fleets should schedule off-peak. The larger V4-Pro at $0.435/$0.87 is a natural step up.
The balanced tier of OpenAI's new GPT-5.6 family, positioned as GPT-5.5-class capability at roughly half the price, with a ~1M context and an aggressive 90% cached-input discount that suits repetitive agent loops. The catch: it is still in limited preview as of July 2026 and per-tier benchmarks are thin, so treat it as a strong bet rather than a settled one. GPT-5.5 at $5/$30 is the proven fallback.
At $0.50/$3 with a 1M context and $0.05/1M cache reads, Gemini 3 Flash is a dependable low-cost workhorse for high-volume agent traffic. Thinking is on by default and those tokens bill as output, so budget for it. If you want more capability from the Google stack, Gemini 3.1 Pro at $2/$12 or the newer Gemini 3.5 Flash at $1.50/$9 are the upgrades.
How We Ranked These Models
Order is computed, not hand-picked. Each model is scored on the published pass-rate suites it has (SWE-bench Verified 0.4, Terminal-Bench 2.1 0.6), renormalised over the suites present, then charged 4 quality points for every doubling of its blended price (3:1 input-to-output tokens). Models with no published score are never given one: they are listed after every scored model, ordered by price, and labelled as unscored. An autonomous agent loops for dozens of tool-using steps, so Terminal-Bench 2.1 leads the weighting at 0.6, price costs a model 4 points of pass rate per doubling because a single run can burn hundreds of thousands of tokens, and the context floor is a full 1M: a model that cannot hold the scratchpad is not a candidate here. See the full ranking method.