Part of Best LLM for Coding
Best LLM for Agentic Coding
Claude Opus 5.5 ranks first for Agentic Coding as of October 20, 2026.[1][2][3][4][5][6] Leads on Terminal-Bench 4.0 with 64.85% and ranks #1 on LMArena's agentic arena (score 20.4) and Design Arena's web-app agent category (Elo 1318), demonstrating strong multi-step tool use and autonomous execution. Claude Fable 5.1 is the next-ranked alternative. Ties for #1 on Design Arena web-app agents (Elo 1318) and ranks #1 on full-stack agents (Elo 1326), with strong performance on LiveBench Agentic Coding at 66.06% (#3 of 59).
- Best open-weight pick: DeepSeek V4.1 Flash (#4)
- Compare Claude Opus 5.5 vs Claude Fable 5.1 →
About this recommendation
- Updated
- Evidence through
- Sources
- 27
- Revision
- v117
Decision audit
Why this result
Inspect the inputs and the computed order behind the recommendation.
Models screened
24
live candidates
Evaluation feeds
7
task-weighted
Winner coverage
69%
intended feed weight
Largest provider share
3 of 8
Anthropic
Anthropic: Claude Opus 5.5 leads the measured task comparison with 4 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 | 31% | #1 | 10/24 |
| 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 | 23/24 |
| Design Arena Full-stack | 14% | not measured | 11/24 |
| LMArena AgentObserved 2026-10-19 · evaluation date not supplied · max effort | 13% | #1 | 20/24 |
| Design Arena Web-appsObserved 2026-10-20 · evaluation date not supplied · anthropic/claude-opus-5.5-20260921 | 11% | #1 | 13/24 |
| LMArena Agent Bash RecoveryObserved 2026-10-08 · evaluation date not supplied · high effort | 11% | #2 | 19/24 |
Provider concentration
Each exact model is scored separately; provider identity is not a ranking input.
- Anthropic3 models
- OpenAI2 models
- deepseek1 model
- Moonshot AI1 model
- xiaomi1 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 69%
#1 Design Arena Web-apps · #1 LMArena Agent
Anthropic · coverage 80%
#1 Design Arena Full-stack · #1 Design Arena Web-apps
Anthropic · coverage 60%
#1 LMArena Agent Bash Recovery · #2 Terminal-Bench
deepseek · coverage 35%
#1 LiveBench Agentic Coding · #7 LMArena Agent Bash Recovery
OpenAI · coverage 60%
#2 LMArena Agent · #3 Terminal-Bench
Moonshot AI · coverage 55%
#2 Design Arena Full-stack · #4 Design Arena Web-apps
OpenAI · coverage 60%
#3 Terminal-Bench · #4 LMArena Agent
xiaomi · coverage 28%
#5 Design Arena Web-apps · #13 LMArena Agent
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 on Terminal-Bench 4.0 with 64.85% and ranks #1 on LMArena's agentic arena (score 20.4) and Design Arena's web-app agent category (Elo 1318), demonstrating strong multi-step tool use and autonomous execution.
Best when: Use for complex multi-agent QA engineering deployments with 1M context windows, as it successfully orchestrated four merge subagents, one style subagent, one obligation auditor and one judge in a clean context.More tips
- Select for creative system-building tasks requiring end-to-end implementation from minimal prompts, such as building complete music composition systems with artifacts and example tracks.
- 02
Claude Fable 5.1Anthropic · $10 in / $50 out per M tokens · 1M context
Ties for #1 on Design Arena web-app agents (Elo 1318) and ranks #1 on full-stack agents (Elo 1326), with strong performance on LiveBench Agentic Coding at 66.06% (#3 of 59).
Best when: Choose for resource generation benchmarks where it wins outright with 10 turns/$0.30 versus 18 turns/$0.85 manual execution, carrying spec through --generate to match columns and make minimal edits.More tips
- Use when you need thorough rule execution, as it follows specifications more rigorously than older models, making it suitable for workflows where strict compliance matters more than flexible interpretation.
- 03
Claude Sonnet 5.5Anthropic · $2 in / $10 out per M tokens · 1M context
Ranks #1 on LMArena Agent's bash-recovery signal (score 15) and #2 on Terminal-Bench 4.0 with 61.82%.
Best when: Select when recovery from failed shell steps is critical, as it leads all models on this specific signal.Watch out for
- Do not assume it will explore beyond explicitly mentioned files, as it failed sibling-sort-bug task 0/2 times by fixing only what the failing test named without opening required sibling files.
- 04
DeepSeek V4.1 Flashdeepseek · $0.056 in / $0.12 out per M tokens · 1.0M context · open-weight
Achieves #1 on LiveBench Agentic Coding with 77.27% and ranks #7 on LMArena Agent's bash-recovery signal (score 7.9), though only #19 on LMArena's agentic arena (score 2.6).
Best when: Select for pure code execution benchmarks where it leads the field on executable JavaScript, TypeScript, and Python tasks.Watch out for
- Disable sub-agents or require explicit user approval before invocation in large repositories (8000+ files), as the model spawns explore sub-agents for tasks solvable with a few main-session searches, causing 40+ minute delays without main-session context.
- 05
GPT-6 AstraOpenAI · $10 in / $50 out per M tokens · 1.1M context
Ranks #2 on LMArena's agentic arena (score 15.1) and #3 on Terminal-Bench 4.0 with 58.18%.
Best when: Deploy as a high-capability workflow and prompt auditor where token usage constraints permit selective use rather than day-to-day default deployment.Watch out for
- Do not use as default day-to-day model due to excessive token consumption that makes it unsuitable for routine workloads.
- Expect more clarifying questions and detailed formatting than other models, with tendency to over-test small changes when following AGENTS.md and skills.
- Avoid for simple well-specified tasks even at xhigh or max reasoning effort, as it may fail to complete and write approximately 1 TB to /tmp during failed execution.
- 06
Kimi K3Moonshot AI · $0.24 in / $11.94 out per M tokens · 1.0M context · open-weight
Ranks #2 on Design Arena full-stack agents (Elo 1315) and #4 on web-app agents (Elo 1292), with LiveBench Agentic Coding at 62.17% (#7 of 59), though LMArena agentic arena is weaker at #23 (score 1.9).
Best when: Select for CSS and SVG generation tasks with adequate timeout configuration (10-30 minutes), where it completes successfully after initial 5-minute timeouts.More tips
- Deploy for PowerShell script development and bug fixes, as demonstrated in 53 KB zero-dependency file development with two passes of fixes.
- 07
GPT-6.1 SolOpenAI · $2 in / $10 out per M tokens · 1.1M context
Ranks #4 on LMArena's agentic arena (score 13.7) and ties for #3 on Terminal-Bench 4.0 with 58.18%.
Best when: Its strongest result for this task is on Terminal-Bench, which measures completing difficult repository and terminal tasks through an agent harness.Watch out for
- Do not use for byte-identical decompilation tasks, as it left functions unmatched with remaining differences documented in output files.
- 08
MiMo-V2.6-Proxiaomi · $0.43 in / $0.87 out per M tokens · 1.1M context · open-weight
Ranks #5 on Design Arena web-app agents (Elo 1289) but drops to #13 on LMArena's agentic arena (score 3.7) with very weak bash-recovery at #24 (score 2.3) and timeout issues on simple tasks.
Best when: Select for web-app agent tasks where Design Arena human preference rankings are the primary metric.Watch out for
- Do not use for simple one-line card tasks, as all three test runs timed out at 180 seconds with no result despite minimal scope (write one file, commit).
- Avoid when lower-tier flash version could handle implementation from plan, as using the pro version wastes expensive tier on mechanical work.
- Watch for security vulnerabilities in shell script execution, as it treats missing exit_code events as success with fail-open default of 0.
Frequently asked
- What is the best model for Agentic Coding?
- Claude Opus 5.5 ranks first as of October 20, 2026. Leads on Terminal-Bench 4.0 with 64.85% and ranks #1 on LMArena's agentic arena (score 20.4) and Design Arena's web-app agent category (Elo 1318), demonstrating strong multi-step tool use and autonomous execution.[1][2][3]
- What is the best alternative to Claude Opus 5.5?
- Claude Fable 5.1 ranks second. Ties for #1 on Design Arena web-app agents (Elo 1318) and ranks #1 on full-stack agents (Elo 1326), with strong performance on LiveBench Agentic Coding at 66.06% (#3 of 59).[4][5][6]
- Which open-weight model ranks highest for Agentic Coding?
- DeepSeek V4.1 Flash is the highest-ranked open-weight model, at #4. Achieves #1 on LiveBench Agentic Coding with 77.27% and ranks #7 on LMArena Agent's bash-recovery signal (score 7.9), though only #19 on LMArena's agentic arena (score 2.6).[7][8][9]
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's agentic arena (score 20.4), measuring tool-use and multi-step task performance from human preference.”
LMArena agentic arena · Benchmark · Oct 19, 2026 - 4
“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 - 5
“Within the 41 catalog models matched to this evaluation: Ranks #1 of 41 on Design Arena's full-stack agent category (Elo 1326), based on blind human preference between agent-built results.”
Design Arena full-stack agents · Benchmark · Oct 14, 2026 - 6
“Within the 59 catalog models matched to this evaluation: Scores 66.06% on LiveBench Agentic Coding (#3 of 59), covering executable JavaScript, TypeScript, and Python tasks.”
LiveBench Agentic Coding · Benchmark · Oct 18, 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
“Within the 48 catalog models matched to this evaluation: Ranks #7 of 48 on LMArena Agent's bash-recovery signal (score 7.9), measuring recovery from failed shell steps.”
LMArena Agent bash recovery · Benchmark · Oct 8, 2026 - 9
“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 - 10
“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 - 11
“<p><strong>Tool:</strong> <a href="https://tools.simonwillison.net/scrimshaw-jukebox">Scrimshaw Jukebox</a></p> <p>I wanted to see if Claude Opus 5.5 could compose music, so <a href="https://claude.ai/share/1f721c20-2499-4d23-b368-3ab57146d956">I tried this</a>:</p> <blockquote> <p><code>I want you to write some computer game music for me. First design simple text based format for the music and build an artifact that can play it out loud - include some example tracks in that artifact</code></p>…”
Engineering publication · Oct 6, 2026 - 12
“In benchmark rounds 3 and 3b (Partisan 0.4.0, Claude Fable 5.1, five runs per task, bench/v2/RESULTS.md in the fixture) agent mode wins the Filament resource outright (10 turns / $0.30 against 18 / $0.85 by hand) because --generate carries the spec: the form and table come out matching the columns and the agent makes three one-line edits. The small scaffolds do not win on turns: on the model task the agent takes 12 turns either way (fewer tokens, $0.39 vs $0.62), and 7 of 15 agent-mode runs…”
icaliman · GitHub · Sep 17, 2026 - 13
Skills: replace weak-model scaffolding with acceptance standards across superstorm, grillstorm and meta-skill
Original-language excerpt:“Problem Statement myskills 的 Skill 与 Protocol 是在会话模型明显更弱的时期写成的。为了补当时模型的短板，文本里加了大量"教做法"的脚手架：写死的模型名与工具名、必须一字不改的固定模板、只认字面形式的机械校验、硬计数上限与下限、每步等人点一次的交互节奏、additionalProperties: false 的固定返回合同。会话模型换成 Claude Fable 5.1 / Codex 新一代之后，这些脚手架反而束缚强模型：它执行规则更彻底，在边界处直接拒跑、拒收、硬凑数，或者忠实照抄过期的模型字面量让整场 run 死掉。同时这些做法把 Skill 特化到了某一个项目的形状，削弱了对任意外部项目的泛化。 cross-exam 已经按"脚手架 vs 闸门"的判据审过并改完（提交 e47d4fb7、b4898631、6ffcde71），九个思维测试场景验证了五条触发、两条推翻。四份审计（meta-skill 核心、meta-skill 能力模板、superstorm 与 product-review、grillstorm）指出同一类问题…”
j08069099777 · GitHub · Sep 7, 2026 - 14
“Within the 48 catalog models matched to this evaluation: Ranks #1 of 48 on LMArena Agent's bash-recovery signal (score 15), measuring recovery from failed shell steps.”
LMArena Agent bash recovery · Benchmark · Oct 8, 2026 - 15
“Follow-up after close: negative result on the always-failing hard tasks, and what changed. - Diagnosis of sibling-sort-bug (0/3): the prompt never mentioned the two sibling files. No model passed it as worded: GLM 5.3 flash 0/3, gpt-6-luna 0/3, claude-sonnet-5.5 0/2. The agent fixed what the failing test named and never opened the other files. misleading-error-shallow-merge is a GLM flash limit: gpt-6-luna gets it 3/3 at about $0.002 per run. - Task change (#452): sibling-sort-bug now ask…”
H4fizWasabie · GitHub · Sep 30, 2026 - 16
[Feature]: 能否禁用子代理，或者由用户控制是否使用子代理执行任务
Original-language excerpt:“Problem / opportunity 当前使用DeepSeek V4.1 Flash时，模型经常会在一个8000文件的仓库中，对于只需要主会话几次搜索就能完成的任务，使用explore子代理。而子代理没有主会话上下文，导致每次子代理需要花费40分钟以上的时间才能完成，极大的浪费时间。我不知道这个是模型的原因，还是OpenBitfun的system prompt导致的 Proposed behavior 因此我希望加一个禁用子代理，或者只在用户同意的情况下才使用子代理执行任务 Product surface Desktop app Details, examples, or constraints OpenBitfun 1.0.1”
skllz-chen · GitHub · Sep 22, 2026 - 17
“Context GPT-6 Astra is available, but its token usage currently makes it unsuitable as the default day-to-day model. Use it selectively as a high-capability workflow and prompt auditor. Official guidance notes that Astra is more sensitive to instructions in AGENTS.md and skills, may ask more clarifying questions, tends toward detailed formatting, and can over-test small changes. These behaviors overlap with prior dotfiles friction around unnecessary STOPs, repeated confirmations, verbose w…”
jjshen-eland · GitHub · Sep 18, 2026 - 18
“What version of Codex CLI is running? codex-cli 0.155.1 What subscription do you have? ChatGPT Pro 20x Which model were you using? gpt-6-astra max What platform is your computer? Linux 6.6.87.2-microsoft-standard-WSL2 x86_64 What terminal emulator and version are you using (if applicable)? Windows Terminal (WSL / PowerShell) Codex doctor report What issue are you seeing? gpt-6-astra cannot complete a simple, well-specified task at either xhigh or max reasoning effort…”
AndyAlvarezDev · GitHub · Sep 19, 2026 - 19
“Just saw the logs, coding demos failed due to the 5 minute task timeout. I have increased it and retesting it now. EDIT: With 10 minutes timeout, the CSS task completed, but the SVG generation task still timed out. Trying again with 30 minutes timeout... EDIT2: It completed (now in only ~9 minutes). It's one of the best hamsters[0]. [0]: https: aibenchy.com compare moonshotai-kimi-k3-max moonshot...”
XCSme · Hacker News · Jul 16, 2026 - 20
“Extremely useful for me at work, thought I'd share: One 53 KB PowerShell file, 0 dependencies. Designed by Opus 5, two passes of bug fixes with GPT 5.6 Terra and Kimi k3 at max reasoning effort.”
guilamu · Hacker News · Sep 11, 2026 - 21
“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 - 22
“Decompile these functions to byte-identical C++. Follow AGENTS.md (claim the issue first) and docs/agent-guide.md. - [ ] 0x4bd160 (580 bytes) Tried by gpt-6.1-sol in #3194. Each file says what still differs; treat it as a starting point, not as correct.”
HectorBailey · GitHub · Oct 1, 2026 - 23
“Decompile these functions to byte-identical C++. Follow AGENTS.md (claim the issue first) and docs/agent-guide.md. - [ ] 0x48a870 (261 bytes) - [ ] 0x48b090 (367 bytes) Tried by gpt-6.1-sol in #3190. Each file says what still differs; treat it as a starting point, not as correct.”
HectorBailey · GitHub · Oct 1, 2026 - 24
“Within the 42 catalog models matched to this evaluation: Ranks #5 of 42 on Design Arena's web-app agent category (Elo 1289), based on blind human preference between agent-built results.”
Design Arena web-app agents · Benchmark · Oct 20, 2026 - 25
“STREAM: swarm: cards The pro spread (nova-sprint routes --tier pro --label , #3641; provider = fnv32a(label) mod N) gives OpenRouter's pro slot to ormimo26pro = openrouter/xiaomi/mimo-v2.6-pro, state held ("too few to say (U=3)"). Evidence, quack runs of 2026-09-25: it ran three one-line cards (write one file, commit) and all three ran out the 180 s wall with no RESULT: - quack-0925b s00-0302-quack-hulk-pro (04:00Z; repo not staged, every model had to clone, but deepseek/qwen/glm still fini…”
rowan-claude · GitHub · Sep 25, 2026 - 26
“Frontier models plan, lower-tier models implement — instruction sheet + global wiring What this is Albert's frontier models — GPT-Sol-6 (medium) in Codex, GLM 5.3 MAX in ZCode, Mimo v2.6 pro in MiMoCode — currently do mechanical implementation themselves even when a fully-specced plan already exists. That wastes the most expensive tier on work a lower tier (GPT-6 Luna, GLM 5.3 Flash, Mimo v2.6 flash) can do from a plan that passes the implementation-plan standard (zero questions, per-step…”
u2giants · GitHub · Sep 24, 2026 - 27
“Latest finding (multi-model security audit) - runShellScript treats a missing exit_code event as success (fail-open default of 0) (_confidence: Medium; models: openrouter/z-ai/glm-5.3-flash:xhigh, xiaomi/mimo-v2.6-pro) - Category: Defensive Programming & Error Handling - Location: src/main/java/sh/createos/jenkins/sandbox/CreateOSApiClient.java:234-264 (runShellScript: 'int exitCode = 0;' with no sentinel distinguishing 'exit_code event received' from 'stream ended without one'; ExecResu…”
pratikbin · GitHub · Sep 24, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.