What is the best LLM for coding agents in 2026?
A coding agent does more than generate a file: it reads a repo, writes code, runs it, observes failure, fixes, repeats. That autonomous action chain is exactly what the Agentic Coding category of our independent benchmark measures — and it is the skill that matters if you automate real development work, not just autocomplete. Scores are much lower than classic Coding: the discipline is young, and the ranking is severe.
The podium: top 3 models in coding agents
Undisputed category leader, DeepSeek earns the spot with consistency across every subtask and a peak of python (90/100). Cost: $0.029/successful task, in the leader pack. Open weights : auto-hébergeable.
66,1 from Anthropic — 11,2 points behind the leader, strong on python (70/100), weaker on typescript (60/100). Watch the price: $1.212/task, much more expensive than the leader for a lower score.
65,2 from Anthropic — 12,1 points behind the leader, strong on javascript (77/100), weaker on typescript (43/100). Watch the price: $0.707/task, much more expensive than the leader for a lower score.
Full Agentic Coding ranking — top 15
| # | Model | Org | Score | Cost/task |
|---|---|---|---|---|
| 1 | deepseek-v4.1-flash-maxopen | DeepSeek | 77,3 | $0.029 |
| 2 | claude-fable-5-1-max-effort | Anthropic | 66,1 | $1.212 |
| 3 | claude-opus-5-max-effort | Anthropic | 65,2 | $0.707 |
| 4 | deepseek-v4-flash-vision-expopen | DeepSeek | 65,1 | $0.051 |
| 5 | smaug-agentic | Other | 64,6 | $0.329 |
| 6 | qwen3.8-maxopen | Alibaba | 64,6 | $0.275 |
| 7 | muse-spark-1.3-xhigh | Other | 64,1 | $0.219 |
| 8 | claude-fable-5-max-effort | Anthropic | 62,2 | $1.478 |
| 9 | kimi-k3 | Moonshot AI | 62,2 | $0.351 |
| 10 | qwen3.8-flash-nextopen | Alibaba | 61,6 | $0.042 |
| 11 | qwen3.8-27bopen | Alibaba | 61,4 | $0.094 |
| 12 | smaug-flash | Other | 61,1 | $0.014 |
| 13 | glm-5.3open | Z.AI | 60,9 | $0.450 |
| 14 | smaug-mini | Other | 60,8 | $0.099 |
| 15 | claude-sonnet-5-xhigh-effort | Anthropic | 59,4 | $0.513 |
Scores out of 100, release 2026-06-25. Cost = dollars per successful task (lower is better). 57 models ranked in this category. See the full benchmark →
How we measure this category
Agentic tasks evaluate the ability to produce working code in a multi-step context, graded by execution. A model that shines at simple completion can collapse when it must hold a thread of decisions across steps — that is what this category reveals.
typescript
python
Look here first if you build CI pipelines, refactoring agents or tools that call the API autonomously. The gap between first and tenth is the largest in the whole benchmark: model choice literally changes your agents’ success rate.
How to read this ranking
This ranking covers 57 models evaluated on the same release, and the gap between first and last reaches 58,7 points — enough to separate professional workloads from casual use. The category median sits at 52,6: any model below that bar must compensate with price or specialization. We also see OpenAI dominating the top of the table (10 models in the top 11), a sign that this skill rewards precise architecture choices more than simply scaling the model. Finally, a category score is never an average of everything: a model that excels here can still be average elsewhere — the other six rankings exist for that.
What changed since the previous release
The current leader (deepseek-v4.1-flash-max, DeepSeek) is a new entry in the ranking: it was not evaluated on release 2026-01-08. That signals a category in flux.
Our recommendation
Bottom line: for raw performance, deepseek-v4.1-flash-max (DeepSeek) leads the category at 77,3/100 — a 12,6-point gap over 5th place (smaug-agentic), an edge that shows up in real deliverable quality on long tasks. On a tight budget, deepseek-v4.1-flash-max ($0.029) is the best score under $0.10/task (77,3/100). Finally, 16 of 57 models are open-weight: if data confidentiality is non-negotiable, self-hosting is a realistic path in this category. Always compare two axes — score and cost per successful task — that is where the choice is made.
Frequently asked questions
What is the best LLM for coding agents in 2026?
On our benchmark (release 2026-06-25), it is deepseek-v4.1-flash-max (DeepSeek) with a score of 77,3/100, ahead of claude-fable-5-1-max-effort (66,1)..
What is a free or budget option for coding agents?
At $0.029 per successful task, deepseek-v4.1-flash-max (DeepSeek) still scores 77,3 — the best performance per dollar in the category, plus open weights you can self-host.
Is the gap between first and tenth noticeable in practice?
It is 15,7 points between deepseek-v4.1-flash-max (77,3) and 10th-place qwen3.8-flash-next (61,6). On a single task the difference often goes unnoticed; across thousands of calls or long chains, those points become different success rates — and a different budget.
How is this ranking produced?
iatrust publishes independent benchmark scores with refreshed questions to avoid training-data contamination, and automatic grading. We add no subjective judgment to the scores; verdicts are computed from subtasks and measured costs. The page regenerates automatically on each new release.
Related reading on the blog
Compare the 57 models across 23 tasks
Full ranking, expandable subtasks, cost per task, release history.