What is the best LLM for reasoning in 2026?
Reasoning is the skill that separates a model predicting the next word from a model that follows a logic chain to a correct conclusion. It is what lets an LLM chain diagnostic steps, weigh a contradiction or deduce what must be true from what is given. We measure this with the Reasoning category of our independent benchmark, with questions renewed each release to rule out memorization.
The podium: top 3 models in reasoning
Undisputed category leader, OpenAI earns the spot with consistency across every subtask and a peak of zebra puzzle (100/100). Cost: $0.736/successful task, in the leader pack.
91,7 from Anthropic — 1,0 points behind the leader, strong on zebra puzzle (100/100). Watch the price: $1.212/task, much more expensive than the leader for a lower score.
91,7 from OpenAI — 1,0 points behind the leader, strong on zebra puzzle (100/100). Cost: $0.507/successful task, in the leader pack.
Full Reasoning ranking — top 15
| # | Model | Org | Score | Cost/task |
|---|---|---|---|---|
| 1 | gpt-6-astra-max | OpenAI | 92,7 | $0.736 |
| 2 | claude-fable-5-1-max-effort | Anthropic | 91,7 | $1.212 |
| 3 | gpt-5.6-sol-max | OpenAI | 91,7 | $0.507 |
| 4 | claude-opus-5-max-effort | Anthropic | 91,2 | $0.707 |
| 5 | kimi-k3 | Moonshot AI | 90,7 | $0.351 |
| 6 | gpt-5.6-terra-max | OpenAI | 90,6 | $0.344 |
| 7 | grok-4.6 | xAI | 90,5 | $0.207 |
| 8 | smaug-agentic | Other | 90,3 | $0.329 |
| 9 | muse-spark-1.2-xhigh | Other | 90,0 | $0.375 |
| 10 | claude-fable-5-max-effort | Anthropic | 89,7 | $1.478 |
| 11 | muse-spark-1.3-xhigh | Other | 89,7 | $0.219 |
| 12 | gpt-5.5-xhigh | OpenAI | 89,7 | $0.436 |
| 13 | gemini-3.8-flash-high | 89,3 | $0.307 | |
| 14 | claude-opus-4-8-max-effort | Anthropic | 89,2 | $0.986 |
| 15 | claude-sonnet-5-xhigh-effort | Anthropic | 88,7 | $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 →
💚 Best value: smaug-flash (Other)
At $0.014 per successful task, smaug-flash reaches 86,2 score — 93% of the leader’s performance for 2% of its price.
How we measure this category
Reasoning tasks cover formal fallacies, causal judgment, multi-step deduction and trap questions. Grading is automatic and binary: right or wrong, with no interpretation margin. That makes this category especially hard to game.
zebra puzzle
spatial
logic with navigation
Reasoning underpins serious use: a legal agent that breaks an implication chain produces a dangerous opinion; an analyst that confuses correlation with causation produces a false diagnosis. If your use involves chained logic steps, this category matters more than the overall score.
How to read this ranking
This ranking covers 57 models evaluated on the same release, and the gap between first and last reaches 32,5 points — enough to separate professional workloads from casual use. The category median sits at 85,6: 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 (gpt-6-astra-max, OpenAI) 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, gpt-6-astra-max (OpenAI) leads the category at 92,7/100 — a 2,0-point gap over 5th place (kimi-k3), an edge that shows up in real deliverable quality on long tasks. On a tight budget, qwen3.8-flash-next ($0.042) is the best score under $0.10/task (87,4/100). 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 reasoning in 2026?
On our benchmark (release 2026-06-25), it is gpt-6-astra-max (OpenAI) with a score of 92,7/100, ahead of claude-fable-5-1-max-effort (91,7)..
What is a free or budget option for reasoning?
At $0.014 per successful task, smaug-flash (Other) still scores 86,2 — the best performance per dollar in the category.
Is the gap between first and tenth noticeable in practice?
It is 3,0 points between gpt-6-astra-max (92,7) and 10th-place claude-fable-5-max-effort (89,7). 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.
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Compare the 57 models across 23 tasks
Full ranking, expandable subtasks, cost per task, release history.