WolfBench @WolfBenchAI

GPT-5.6 Sol took the top three spots on WolfBench: Codex: 86.74% Terminus-2: 85.17% Hermes: 84.49% So far, so benchmarky. Then I checked the tokens. With the same Sol model at maximum reasoning, Codex used 83.8 million tokens per run. Hermes used 170.9 million. Twice the tokens for a 2.25 percentage-point lower score. Hermes recorded $173.41 per run. Codex comes out at an estimated ~$89.79-$95.06. (That Codex number is reconstructed from token usage, not billed spend, but even its upper bound is 45% lower.) The GPT-5.5 comparison makes it even weirder: With Codex, GPT-5.6 gained 8.09 points, used 20% fewer tokens, and appears roughly half as expensive. With Terminus-2, it gained 8.31 points while cost fell 26.5%. With Hermes, it gained 10.34 points - but used 68% more tokens and cost 47% more. So is GPT-5.6 cheaper than GPT-5.5? Depends entirely on the agent. One last oddity: Across 15 Sol runs, the "configure git webserver" task was never solved. Terra solved it once. Luna solved it every time. The smaller variants are not simply weaker copies of Sol. My takeaway: Codex + Sol is the strongest full-agent stack we tested. Terminus-2 + Sol is the cleanest reference baseline. And for Hermes, I would seriously consider Terra: only 3.6 points behind Sol, but 44% cheaper. Benchmark the stack, not just the model! And run it more than once. Full results on WolfBench.ai:

@Zai_org Most benchmark charts show only average scores, but WolfBench's Solid Base metric shows how consistent a model truly is: the percentage of tasks it solved in EVERY one of the 5 runs. By that measure, GLM-5.2 ranks #1, solving more tasks consistently than any other model I tested.

@Zai_org WolfBench.ai is not just a leaderboard - it is an interactive results viewer. Enable 3D Tokens + Cost view (tokens = depth, cost = shadow) to see efficiency at a glance: top 3 performance with low token use and lowest spend. It is the cheapest model in the top 10+.

WolfBench results for @Zai_org's new GLM-5.2: #1 open-weight model #3 overall Cheapest among the top 10 A massive leap forward for open models - and for capable, affordable intelligence you can truly own. 🚀

Explore the full results at WolfBench.ai: compare models and agents in the interactive chart, and click any bar to open the corresponding evals and traces in @weave_wb for deeper inspection.

Even without refusals, real agentic failure patterns remained. Fable's biggest weakness was overconfident self-verification: it often declared victory too early once the solution looked plausible, while the actual benchmark checks still caught wrong output, messy cleanup, missed edge cases, or slow code. Claude Fable 5 may be exceptional, but it is currently not the best fit as a general-purpose agentic daily driver. It is too expensive and too refusal-prone to turn its strengths into efficient, reliable agentic work.

We spent $11,081.12 evaluating @AnthropicAI's Claude Fable 5 on WolfBench. Our most expensive benchmark yet. And it did not even top the charts. Not because it lacked capability, but because it kept refusing. Details in thread: 🧵

For benchmarks, I keep agent versions stable so results stay comparable. But new models can expose agent-side bugs. Here, updating @openclaw from 2026.3.11 to 2026.4.23 lifted Kimi K2.6 from 4% to 60% on @WolfBenchAI due to crucial fixes in how the agent handles its tool calling.

Let's compare the WolfBench top model, GPT-5.5, with our #2, Claude Opus 4.7: - @openclaw is still better on Opus 4.7 than on GPT-5.5: 75% vs. 70%, with a slightly higher ceiling and base. This is also the third-highest score across all models and agents - only @cursor_ai and Terminus-2 (the official @terminalbench 2.0 test harness) rank higher, both at 77%. - When no reasoning level is set, the OpenAI API defaults GPT-5.5 to medium, while the Anthropic API defaults Opus 4.7 to no thinking. That's why Terminus-2 and Hermes Agent have different effort levels. - Note that higher effort levels don't necessarily improve scores in agentic benchmarks - thinking harder can actually make the model dumber: wandb.ai/wandb_fc/wolfb… - Still have to evaluate Cursor with Opus 4.7; with 4.6, it got 63%.

GPT-5.5 takes over WolfBench! It’s now the #1 model, ahead of Claude Opus 4.7 and 4.6, GPT-5.4, Sonnet 4.6, Kimi K2.6, Gemini 3.1 Pro, and more. Notable findings after 30 runs (40h runtime, >1.7B tokens, ~$3K cost): - @OpenAI's GPT-5.5 is the best model we ever tested. - @cursor_ai's Agent CLI (CA) is the best agent we ever tested. - @NousResearch's Hermes Agent (HA) outperformed OpenClaw (OC). - With Hermes, going from medium to xhigh reasoning only improved consistency, not capability. Note: This is WolfBench, where we look at more than just the average score, because one metric is not enough. The golden ∅ score is the actual 5-run average, which most other benchmarks report as their only score. ★ shows the ceiling (what percentage of the full benchmark this model+agent combination solved at least once across all runs). ■ shows the solid base (what percentage of the full benchmark it solved consistently in every run).

@OpenAI Visit wolfbench.ai for the full lineup of models and agents. The site is fully interactive: filter and sort by models, agents, metrics, and scores, or click any bar to jump straight to the corresponding @weave_wb evals and traces. Full transparency for all 300+ runs!

the super interesting thing that I find not enough people talking about is OpenClaw topping the T2 leaderboard for Opus 4.7 with thinking off (@WolfBenchAI eval harness) l - OC also a generic harness unlike the other harnesses in the below lb which are likely benchmaxxed for coding

Niels Rogge @NielsRogge

3 months ago

FYI Claude Code is mostly a vibe-coded product (as they say, 100% written by Claude) It's the worst harness for Opus 4.6 among ANY harness on Terminal-Bench 2

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@Aiolias_ @WolfBenchAI That's fair. But this one is a bit different and tells a realistic story (my custom testing pipeline share more than half of what it uses) .

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@prosperous727 @grok @NousResearch TY sir!

Hermes Agent outperformed Claude Code and OpenClaw as an agentic harness for both Opus 4.6 and GPT-5.4 on 89 real-world tasks. Not just higher scores but a higher floor. More tasks solved reliably, every single run. @teknium @NousResearch really cooked with this one. 🔥

Not even ready for something to outperform Claude 🤯

WolfBench @WolfBenchAI

4 months ago

Hermes Agent outperformed Claude Code and OpenClaw as an agentic harness for both Opus 4.6 and GPT-5.4 on 89 real-world tasks. Not just higher scores but a higher floor. More tasks solved reliably, every single run. @teknium @NousResearch really cooked with this one. 🔥

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며칠전부터 자꾸 Hermes 에이전트에 신경이 쓰인다. 사실 OpenClaw가 좀 더 오래 시장을 장악할 줄 알았는데, 아직 검증은 안됐지만 강력한 경쟁자가 들어온 것 같다. 미국에 NousResearch라는 팀이 있다. Nous Research는 오픈소스 AI 분야에서 가장 앞서가는 스타트업/연구 팀 중 하나이고. 사용자가 직접 제어할 수 있는 “user-aligned(사용자 정렬)” 모델로 큰 주목을 받고 있다. 그들이 만든 Hermes Agent가 89개 실제 작업 테스트에서 Claude Code와 OpenClaw를 앞질렀다. 점수만 높은 게 아니라 "바닥"이 높았다. 매번 더 많은 작업을 안정적으로 완료했다는 뜻이다. 그럼 왜 그런 결과가 나왔을까? 핵심은 하네스다. 하네스는 AI 모델을 감싸는 틀이다. 같은 Opus 4.6이라도 어떤 하네스에 넣느냐에 따라 결과가 달라진다. Hermes의 주장은 "우리 모델이 더 좋다"가 아니다. "같은 모델을 더 잘 쓰는 구조를 만들었다"는 거에 의미가 있다. 그 구조의 핵심은 학습 루프라는 핵심 기술이다. Claude Code는 매번 새로 시작한다. OpenClaw는 MEMORY.md로 기억을 수동 관리한다. 기억을 유지하게 셋팅을 하는 것은 여전히 인간 몫이다. Hermes는 시스템이 조금 다르다. 복잡한 작업이 끝나면 에이전트가 자율적으로 재사용 가능한 스킬을 생성하고 저장한다. 뭘 기억할지, 뭘 스킬로 만들지 에이전트가 스스로 판단하는 구조다. 공식 설명 그대로 "built-in learning loop", "autonomous skill creation", "skills self-improve during use." 인간이 아무것도 안 해도 에이전트가 점점 영리해진다. 커뮤니티에서는 Hermes를 "Claude Code 스타일 CLI와 OpenClaw 스타일 메시징 에이전트의 중간"으로 부르기도 한다. 둘 다 되려 한다는 뜻이다. 터미널에서도, 텔레그램에서도, VPS에서도. v0.2.0 출시 이후 빠르게 스타 10,000개를 넘겼고, 현재 22,000개를 돌파했다. 개인적으로도 이 전략은 영리하다고 생각한다. 사람들은 "3% 더 똑똑한 모델"보다 "나를 기억하는 에이전트"라는 스토리에 더 끌린다. Hermes의 슬로건 "The agent that grows with you"는 성능이 아니라 나와 에이전트의 관계를 판다. AI 에이전트의 다음 전쟁터는 모델 성능이 아니다. 얼마나 빠르게 배우고, 얼마나 오래 기억하느냐가 아닐까? 그리고 개인 맞춤 에이전트 브랜드가 점점 다가오는 느낌이다. 나도 오늘 한번 설치하고 돌려보려고 한다.

WolfBench @WolfBenchAI

4 months ago

Hermes Agent outperformed Claude Code and OpenClaw as an agentic harness for both Opus 4.6 and GPT-5.4 on 89 real-world tasks. Not just higher scores but a higher floor. More tasks solved reliably, every single run. @teknium @NousResearch really cooked with this one. 🔥

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@Zeneca Here are our latest benchmarks on Agent Harnesses for your evaluation! x.com/WolfBenchAI/st…

WolfBench @WolfBenchAI

4 months ago

Hermes Agent outperformed Claude Code and OpenClaw as an agentic harness for both Opus 4.6 and GPT-5.4 on 89 real-world tasks. Not just higher scores but a higher floor. More tasks solved reliably, every single run. @teknium @NousResearch really cooked with this one. 🔥

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@vectro We just published our evals benchmarks for agent harnesses. You can see for yourself below, but Hermes is really powerful straight out of the box. x.com/WolfBenchAI/st…

WolfBench @WolfBenchAI

4 months ago

Hermes Agent outperformed Claude Code and OpenClaw as an agentic harness for both Opus 4.6 and GPT-5.4 on 89 real-world tasks. Not just higher scores but a higher floor. More tasks solved reliably, every single run. @teknium @NousResearch really cooked with this one. 🔥

8 20 173 94K 114