What is the best LLM for coding in 2026?
Choosing a model for software development should not rely on marketing demos. We compare LLMs on our independent benchmark, with regularly refreshed questions to avoid dataset contamination — a model cannot simply have memorized the answers. The Coding category measures two distinct skills: code generation (writing a full program from a spec) and completion (finishing an existing context, the daily copilote use case).
The podium: top 3 models in coding
Undisputed category leader, Anthropic earns the spot with consistency across every subtask and a peak of code generation (90/100). Cost: $1.212/successful task, in the leader pack.
86,0 from Anthropic — 0,4 points behind the leader, strong on code generation (92/100). Watch the price: $1.478/task, slightly more expensive than the leader for a lower score.
83,9 from OpenAI — 2,4 points behind the leader, strong on code completion (85/100). Cost: $0.507/successful task, in the leader pack.
Full Coding ranking — top 15
| # | Model | Org | Score | Cost/task |
|---|---|---|---|---|
| 1 | claude-fable-5-1-max-effort | Anthropic | 86,4 | $1.212 |
| 2 | claude-fable-5-max-effort | Anthropic | 86,0 | $1.478 |
| 3 | gpt-5.6-sol-max | OpenAI | 83,9 | $0.507 |
| 4 | gpt-5.2-codex | OpenAI | 83,6 | $0.187 |
| 5 | gpt-5.6-luna-max | OpenAI | 82,9 | $0.168 |
| 6 | smaug-agentic | Other | 82,5 | $0.329 |
| 7 | gpt-5.5-xhigh | OpenAI | 82,1 | $0.436 |
| 8 | claude-opus-4-7-xhigh-effort | Anthropic | 82,1 | $0.528 |
| 9 | claude-opus-4-8-max-effort | Anthropic | 81,8 | $0.986 |
| 10 | claude-opus-5-max-effort | Anthropic | 81,4 | $0.707 |
| 11 | kimi-k3 | Moonshot AI | 81,4 | $0.351 |
| 12 | muse-spark-1.3-xhigh | Other | 81,1 | $0.219 |
| 13 | claude-sonnet-5-xhigh-effort | Anthropic | 80,7 | $0.513 |
| 14 | gpt-6-astra-max | OpenAI | 80,4 | $0.736 |
| 15 | deepseek-v4.1-flash-maxopen | DeepSeek | 80,0 | $0.029 |
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 →
💚 Best value: deepseek-v4.1-flash-max (DeepSeek)
At $0.029 per successful task, deepseek-v4.1-flash-max reaches 80,0 score — 93% of the leader’s performance for 2% of its price. And its open weights let you self-host.
How we measure this category
The Coding category scores models on two task families, graded automatically by running produced code against unit tests — no subjective judgment. Unlike HumanEval or MBPP, now saturated and likely present in most training sets, this benchmark renews its questions every release. A score here reflects real ability to reason about unseen code.
code completion
In practice, the gap between the top five barely shows on a ten-line snippet; it becomes decisive on multi-file refactors or long-chain debugging. That is why we also show cost per successful task: the best model is not always the one you can call a thousand times a day.
How to read this ranking
This ranking covers 57 models evaluated on the same release, and the gap between first and last reaches 21,0 points — enough to separate professional workloads from casual use. The category median sits at 77,5: 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 (claude-fable-5-1-max-effort, Anthropic) 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, claude-fable-5-1-max-effort (Anthropic) leads the category at 86,4/100 — a 3,5-point gap over 5th place (gpt-5.6-luna-max), 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 (80,0/100). 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 in 2026?
On our benchmark (release 2026-06-25), it is claude-fable-5-1-max-effort (Anthropic) with a score of 86,4/100, ahead of claude-fable-5-max-effort (86,0)..
What is a free or budget option for coding?
At $0.014 per successful task, smaug-flash (Other) still scores 77,1 — the best performance per dollar in the category.
Is the gap between first and tenth noticeable in practice?
It is 4,9 points between claude-fable-5-1-max-effort (86,4) and 10th-place claude-opus-5-max-effort (81,4). 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
Claude Fable 5.1 et Mythos 5.1 : Anthropic pousse son duo coding & recherche
5 septembre 2026
Modèles d’IA chinois en 2026 : DeepSeek, Qwen, Kimi, GLM… lequel pour quoi ?
5 septembre 2026
Compare the 57 models across 23 tasks
Full ranking, expandable subtasks, cost per task, release history.