Part of Best LLM for AI Agents
Best LLM for Autonomous Agents
Claude Opus 5.5 ranks first for Autonomous Agents as of October 20, 2026.[1][2][3][4][5][6] Leads multiple agent benchmarks including Design Arena web-app agent (Elo 1318), Terminal-Bench 4.0 (#1, 64.85%), and both LMArena agentic measures (#1, scores 21.4 and 20.4). Watch out: Watch for unparseable JSON output in autonomous pipelines that can cause system failures requiring manual restart. Claude Sonnet 5.5 is the next-ranked alternative. It scores 61.82% ± 2.94 (95% interval) on Terminal-Bench 4.0 (#2 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks.
- Best open-weight pick: DeepSeek V4.1 Flash (#6)
- Compare Claude Opus 5.5 vs Claude Sonnet 5.5 →
About this recommendation
- Updated
- Evidence through
- Sources
- 24
- Revision
- v114
Decision audit
Why this result
Inspect the inputs and the computed order behind the recommendation.
Models screened
24
live candidates
Evaluation feeds
6
task-weighted
Winner coverage
67%
intended feed weight
Largest provider share
3 of 8
Anthropic
Anthropic: Claude Opus 5.5 leads the measured task comparison with 3 independent evaluation families. Community volume, release age and usage popularity add no ranking points.
Sources evaluated
These are the weights used in scoring. Missing measurements are omitted from each model’s average. A feed with no result for any model released in the last 60 days is stale and does not score until it catches up.
| Evaluation feed | Weight | Winner result | Field measured |
|---|---|---|---|
| Terminal-Bench (4.0)Observed 2026-10-20 · evaluated 2026-10-06 · Claude Code; max effort · 95% interval ±3.11 | 47% | #1 | 14/24 |
| SWE-rebenchstale | 0% | no current result | 0/24 |
| LiveBench Agentic Coding (2026-06-25)Observed 2026-10-18 · evaluation date not supplied · claude-opus-5-5-max-effortfailing; last successful check 2026-10-18 | 20% | #2 | 24/24 |
| LMArena Agent Task OutcomeObserved 2026-10-19 · evaluation date not supplied · max effort | 20% | #1 | 24/24 |
| LMArena AgentObserved 2026-10-19 · evaluation date not supplied · max effort | 13% | #1 | 24/24 |
| OSWorld Verifiedstale | 0% | no current result | 0/24 |
Provider concentration
Each exact model is scored separately; provider identity is not a ranking input.
- Anthropic3 models
- OpenAI2 models
- deepseek1 model
- Moonshot AI1 model
- Z.ai1 model
Decision table
Every published model is shown in computed order. Practitioner sources are distinct community threads, not the citations repeated in the prose below.
Anthropic · coverage 67%
#1 LMArena Agent · #1 LMArena Agent Task Outcome
Anthropic · coverage 67%
#2 LMArena Agent Task Outcome · #2 Terminal-Bench
Anthropic · coverage 67%
#3 LiveBench Agentic Coding · #3 LMArena Agent
OpenAI · coverage 67%
#2 LMArena Agent · #3 Terminal-Bench
OpenAI · coverage 67%
#3 Terminal-Bench · #4 LMArena Agent
deepseek · coverage 36%
#1 LiveBench Agentic Coding · #13 LMArena Agent Task Outcome
Moonshot AI · coverage 36%
#7 LiveBench Agentic Coding · #14 LMArena Agent Task Outcome
Z.ai · coverage 67%
#9 Terminal-Bench · #10 LiveBench Agentic Coding
Task score averages the available, task-weighted measurements. Benchmark positions compare models matched to our catalog. Models measured by at least two independent evaluator families are considered first. Missing tests, practitioner report counts, popularity and release age add no points. Coverage describes evidence breadth, not a probability of correctness. Scores within two points are shown as close under this policy; this is not a statistical confidence interval. When the leading scores are that close, the model measured by more evaluator families ranks first.
- 01
Claude Opus 5.5Anthropic · $4 in / $20 out per M tokens · 1M context
Leads multiple agent benchmarks including Design Arena web-app agent (Elo 1318), Terminal-Bench 4.0 (#1, 64.85%), and both LMArena agentic measures (#1, scores 21.4 and 20.4).
Best when: Use for terminal-based autonomous tasks where Claude Code integration matters, scoring 64.85% on Terminal-Bench 4.0.More tips
- Deploy when human preference for agent-built results is the success metric, ranking #1 on Design Arena web-app agent category.
- Select for multi-step tool-use workflows where LMArena's explicit task completion signal ranks it #1 of 48.
- Employ as an orchestrator coordinating multiple subagents in clean contexts for deployment workflows.
Watch out for
- Watch for unparseable JSON output in autonomous pipelines that can cause system failures requiring manual restart.
- Expect weaker high-level planning without detail-filling compared to alternatives like Fable.
- Implement turn limits manually, as the model can enter infinite loops requiring manual intervention after 38+ turns.
- Anticipate blocking behavior on long-running commands with poor visibility into execution progress.
- 02
Claude Sonnet 5.5Anthropic · $2 in / $10 out per M tokens · 1M context
Claude Sonnet 5.5 scores 61.82% ± 2.94 (95% interval) on Terminal-Bench 4.0 (#2 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks.
Best when: Its strongest result for this task is on Terminal-Bench, which measures completing difficult repository and terminal tasks through an agent harness. - 03
Claude Fable 5.1Anthropic · $10 in / $50 out per M tokens · 1M context
Claude Fable 5.1 ranks #3 of 48 on LMArena's agentic arena (score 14.3), measuring tool-use and multi-step task performance from human preference.
Best when: Its strongest result for this task is on LMArena Agent, which measures tool calling and multi-step control across a long task.No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.
- 04
GPT-6 AstraOpenAI · $10 in / $50 out per M tokens · 1.1M context
GPT-6 Astra ranks #2 of 48 on LMArena's agentic arena (score 15.1), measuring tool-use and multi-step task performance from human preference.
Best when: Its strongest result for this task is on LMArena Agent, which measures tool calling and multi-step control across a long task. - 05
GPT-6.1 SolOpenAI · $2 in / $10 out per M tokens · 1.1M context
GPT-6.1 Sol scores 58.18% ± 3.11 (95% interval) on Terminal-Bench 4.0 (#3 of 23) at max reasoning effort, using Codex, measuring a model-and-agent configuration on reproducible terminal tasks.
Best when: Its strongest result for this task is on Terminal-Bench, which measures completing difficult repository and terminal tasks through an agent harness.No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.
- 06
DeepSeek V4.1 Flashdeepseek · $0.056 in / $0.12 out per M tokens · 1.0M context · open-weight
Leads LiveBench Agentic Coding at 77.27% (#1 of 59) despite lower human preference rankings, with verified autonomous file manipulation and report drafting capabilities.
Best when: Select for executable code generation tasks where LiveBench Agentic Coding performance matters, scoring 77.27% at #1.More tips
- Deploy for autonomous remote file operations where it has modified configuration files and parsed logs without human intervention.
Watch out for
- Expect lower human preference rankings on LMArena agentic arena (#19, score 2.6) and explicit task completion (#13, score 6.6).
- 07
Kimi K3Moonshot AI · $0.24 in / $11.94 out per M tokens · 1.0M context · open-weight
Ranks #2 on Design Arena full-stack agent (Elo 1315) with strong web-app performance.
Best when: Select for full-stack autonomous development where Design Arena ranks it #2 of 41.More tips
- Use for web-app agent tasks where it ranks #4 of 42.
Watch out for
- Watch for parent agent failures to synthesize sub-agent outputs in Swarm architectures, leaving users to manually aggregate results.
- 08
GLM 5.3Z.ai · $0.079 in / $1.40 out per M tokens · 1.0M context · open-weight
Moderate LiveBench performance with critical failures in sub-agent reliability, empty responses, and Swarm integration.
Best when: Use for LiveBench Agentic Coding where it scores 60.91% (#10 of 59).Watch out for
- Watch for zero-reasoning-token outputs and completely empty responses that terminate sub-agents.
- Expect parent agent failures to synthesize sub-agent outputs in Swarm architectures.
- Handle parser failures where missing conclusions cause entire rounds to be discarded despite partial success.
- Note that helpfulness tuning can erode ability to distinguish verified from unverified information.
Frequently asked
- What is the best model for Autonomous Agents?
- Claude Opus 5.5 ranks first as of October 20, 2026. Leads multiple agent benchmarks including Design Arena web-app agent (Elo 1318), Terminal-Bench 4.0 (#1, 64.85%), and both LMArena agentic measures (#1, scores 21.4 and 20.4).[1][2][3][4]
- What should I watch out for with Claude Opus 5.5?
- Watch for unparseable JSON output in autonomous pipelines that can cause system failures requiring manual restart.[5]
- What is the best alternative to Claude Opus 5.5?
- Claude Sonnet 5.5 ranks second. Claude Sonnet 5.5 scores 61.82% ± 2.94 (95% interval) on Terminal-Bench 4.0 (#2 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks.[6]
- Which open-weight model ranks highest for Autonomous Agents?
- DeepSeek V4.1 Flash is the highest-ranked open-weight model, at #6. Leads LiveBench Agentic Coding at 77.27% (#1 of 59) despite lower human preference rankings, with verified autonomous file manipulation and report drafting capabilities.[7][8]
Sources
- 1
“Within the 42 catalog models matched to this evaluation: Ranks #1 of 42 on Design Arena's web-app agent category (Elo 1318), based on blind human preference between agent-built results.”
Design Arena web-app agents · Benchmark · Oct 20, 2026 - 2
“Within the 23 catalog models matched to this evaluation: Scores 64.85% ± 3.11 (95% interval) on Terminal-Bench 4.0 (#1 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks.”
Terminal-Bench · Benchmark · Oct 20, 2026 - 3
“Within the 48 catalog models matched to this evaluation: Ranks #1 of 48 on LMArena Agent's explicit task-outcome signal (score 21.4), measuring tasks users marked as completed.”
LMArena Agent task outcome · Benchmark · Oct 19, 2026 - 4
“Within the 48 catalog models matched to this evaluation: Ranks #1 of 48 on LMArena's agentic arena (score 20.4), measuring tool-use and multi-step task performance from human preference.”
LMArena agentic arena · Benchmark · Oct 19, 2026 - 5
“Objective A targeted reviewer launched by agent-launcher always returns a parseable answer, or leaves enough evidence to say why it did not. Intent The saga-review-corpus pilot (saga-review-corpus#3, on infiquetra-agent-plugins b04ff8b) stopped twice on the same tuning case because the primary Claude reviewer's final message was not JSON: "exit": 3, "error": "the final message is not JSON" (Opus 5.5 high, 12 turns, 9,633 output tokens, $0.73). One of the first six reviews failed this…”
namredips · GitHub · Oct 16, 2026 - 6
“Within the 23 catalog models matched to this evaluation: Scores 61.82% ± 2.94 (95% interval) on Terminal-Bench 4.0 (#2 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks.”
Terminal-Bench · Benchmark · Oct 20, 2026 - 7
“Within the 59 catalog models matched to this evaluation: Scores 77.27% on LiveBench Agentic Coding (#1 of 59), covering executable JavaScript, TypeScript, and Python tasks.”
LiveBench Agentic Coding · Benchmark · Oct 18, 2026 - 8
[SM75 / RTX 2070] 黑神话：悟空实测：物理 Turing 上 SM75 路由与 cubin 内核均正常，附四档倍率帧率与生成帧级 GPU 耗时
Original-language excerpt:“关于本报告的性质与来源（请先看这一段） - 本报告由 AI agent 起草，模型为 deepseek v4.1 flash + Codex。 - 所有数据均为真实实机跑出，没有估算、没有编造。 - 分工方式：agent 通过网络远程把 version.dll 与 INI 放进游戏目录、按轮次修改 dlssg_sm86.ini、并在每轮结束后读取与解析 dlssg_sm86\logs\*.jsonl；人类负责在游戏内执行基准测试、把结果界面截图回传。 - 测试性质：只运行了游戏内置的性能基准测试，没有实际游玩。 所有结论都仅限于基准测试场景，长期稳定性、实际手感、过场动画与长时间显存行为均未覆盖。 - 画质方面没有做逐帧对比（无截图或录屏对照），只有主观观察，见文末。 - 与 issue #99 / #83 里提到的 20 系"闪屏"不同，本配置下没有出现闪屏；但观察到轻微拖影。 --- 摘要 在物理 RTX 2070（TU106 / sm_75 / 8GB）上，使用 DLSSG Native 0.2.4 + `…”
linglu114 · GitHub · Sep 12, 2026 - 9
“KAIF 2.7 update field report — QA_Engineer > Deployment: sphere qa-engineering · language ru (i18n: translated) · agents claude-code + > codex + 3 mirrors · macOS 26.0.1 arm64 · Node v23.11.0 · executed by Claude Opus 5.5 (1M context) in > Claude Code, with four merge subagents, one style subagent, one obligation auditor and one judge, each > in a clean context. Route: thin KAIF.md 2.7 (fresh core) bound to a sandbox rehearsal, 2.4 → 2.7, > tracking: origin throughout. Writt…”
MikalaiKryvusha · GitHub · Sep 23, 2026 - 10
“I find fable better at high level planning, as in planning a task without filling in all the details. Opus doesn’t seem to be great at this, but other models aren’t either.”
sutterd · Hacker News · Oct 3, 2026 - 11
“Following #2: there's no limit on how many turns a run can take, so a model stuck in a loop keeps going until someone restarts the bot. Today's loop was only stopped by hand after 38 turns. A cap, even a generous one, would have ended it on its own. Once the cap is hit, the bot should probably tell the channel it gave up rather than go quiet. 🤖 Claude Opus 5.5”
dcabo · GitHub · Sep 23, 2026 - 12
“One of my key complaints with Opus 5.5 so far has been that sometimes it'll execute long-running commands in a way that is blocking any further input or it starts doing stuff without providing much visibility. Is this a problem with the model or the harness in your opinion?”
Lucasoato · Hacker News · Sep 28, 2026 - 13
“Within the 48 catalog models matched to this evaluation: Ranks #3 of 48 on LMArena's agentic arena (score 14.3), measuring tool-use and multi-step task performance from human preference.”
LMArena agentic arena · Benchmark · Oct 19, 2026 - 14
“Within the 48 catalog models matched to this evaluation: Ranks #2 of 48 on LMArena's agentic arena (score 15.1), measuring tool-use and multi-step task performance from human preference.”
LMArena agentic arena · Benchmark · Oct 19, 2026 - 15
“Within the 23 catalog models matched to this evaluation: Scores 58.18% ± 3.11 (95% interval) on Terminal-Bench 4.0 (#3 of 23) at max reasoning effort, using Codex, measuring a model-and-agent configuration on reproducible terminal tasks.”
Terminal-Bench · Benchmark · Oct 20, 2026 - 16
“Within the 48 catalog models matched to this evaluation: Ranks #19 of 48 on LMArena's agentic arena (score 2.6), measuring tool-use and multi-step task performance from human preference.”
LMArena agentic arena · Benchmark · Oct 19, 2026 - 17
“Within the 48 catalog models matched to this evaluation: Ranks #13 of 48 on LMArena Agent's explicit task-outcome signal (score 6.6), measuring tasks users marked as completed.”
LMArena Agent task outcome · Benchmark · Oct 19, 2026 - 18
“Within the 41 catalog models matched to this evaluation: Ranks #2 of 41 on Design Arena's full-stack agent category (Elo 1315), based on blind human preference between agent-built results.”
Design Arena full-stack agents · Benchmark · Oct 14, 2026 - 19
“Within the 42 catalog models matched to this evaluation: Ranks #4 of 42 on Design Arena's web-app agent category (Elo 1292), based on blind human preference between agent-built results.”
Design Arena web-app agents · Benchmark · Oct 20, 2026 - 20
（Bug 回归）Swarm 主代理用子会话引用代替完整综合，形式覆盖仍可通过聚合门禁
Original-language excerpt:“问题 Swarm 子代理已经分别完成完整调研后，主代理仍可能只给一段很短的总体判断，然后写“完整报告见各子代理会话的最终回复”，把本应由主代理完成的阅读、整合与交付工作留给用户。 截图中的具体表现： - 主代理只在当前答复中链接了 GLM-5.3 调研报告。 - Hy4-preview 与 Kimi K3 的完整内容没有进入主答复，而是让用户分别去 260903-gentle-sun / 260903-steady-wave 查看。 - 用户要得到完整横向报告，仍需自己打开多个子会话、阅读并拼接结论。 这不是单纯缺少会话超链接。即使子会话入口可点击，主代理也不应把子代理当作直接面向用户的最终交付者。子会话应提供核验与追溯，主代理必须交付一份自包含的综合结果。 与已有 Issue 的关系 - #229 要求主代理覆盖全部 worker，并由 PR #236 增加了聚合合同和确定性门禁；该 Issue 已关闭。 - #228 / #207 解决子代理入口保留和悬浮预览，不能替代主代理的内容整合。 - 本 Issue 是 #229 修复后的业务回归：当前回答可以在形式上…”
0-CHENAI · GitHub · Sep 3, 2026 - 21
“Within the 59 catalog models matched to this evaluation: Scores 60.91% on LiveBench Agentic Coding (#10 of 59), covering executable JavaScript, TypeScript, and Python tasks.”
LiveBench Agentic Coding · Benchmark · Oct 18, 2026 - 22
“Summary After shipping #9 / #10 (stripPriorTurnReasoning, release 0.10.0-strip9), we observed degraded GLM-5.3 behavior on the openai-completions path: a large fraction of assistant responses arrive with zero reasoning tokens while still containing content/tool calls, and in the worst case a response is completely empty (thinking=0 / text=0 / tool_calls=0), which kills subagents ("empty response, then exit"). Action taken: compat.stripPriorTurnReasoning flipped back to false…”
ziliangpeng · GitHub · Oct 12, 2026 - 23
“Field report from mepro, 2026-09-01..02. Sibling of #150 but a different failure: #150's parse dies outright (Expecting property name…); here the parser gets far enough to count examined entries and findings, then throws the whole round away for a missing conclusion. The symptom Seat: zai-anthropic/glm-5.3 (a gating seat on mepro, with no failover — every backup entry is inert since the 2026-08-25 all-agentic conversion, so "promote its backup" is unavailable). Rate | mepro…”
lemehmet · GitHub · Sep 2, 2026 - 24
“Context — WHY mika-secretary (MSC) Q4 relais 2026-08-23 : au smoke test 20/08, Mika a séparé d'elle-même ligne-par-ligne ce qu'elle avait vérifié à la source de ce qu'elle tenait d'une convergence de snippets — marquant « non vérifié » y compris sur un montant qu'elle donnait pourtant juste, et refusant de conclure. C'est le comportement qu'on veut. C'est typiquement ce qu'un tuning « sois plus utile / plus direct » érode en premier. Verrou eval AVANT rollout GLM-5.3 famille. Evidence…”
samidarko · GitHub · Aug 23, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.