Best AI Model for Logical Reasoning and Math in 2026

Not all AI models are created equal when it comes to thinking through hard problems. Some breeze through calculus but stumble on logic puzzles. Others nail deductive reasoning but fall apart on multi-step word problems.

We tested six leading models across three categories of reasoning tasks in Promptster to find out which ones actually deserve the "reasoning model" label in 2026.

The Models We Tested

All tests used temperature 0 (fully deterministic), max tokens 4,000, and the same system prompt: "Think step by step. Show your reasoning before giving a final answer." For OpenAI's o4-mini, which does not accept a temperature parameter, we used reasoning_effort: medium instead, which balances depth with speed.

Test Categories

Category 1: Logic Puzzles

Five classic logic puzzles, ranging from simple deduction to complex constraint satisfaction:

Prompt example:
"Five houses in a row are painted different colors. The English person
lives in the red house. The Spanish person owns a dog. Coffee is drunk
in the green house. The Ukrainian drinks tea. The green house is
immediately to the right of the ivory house... Who owns the zebra?"

Category 2: Mathematical Proofs

Four proof tasks: induction, contradiction, combinatorics, and a real analysis epsilon-delta proof.

Prompt example:
"Prove that there are infinitely many prime numbers.
Use proof by contradiction. Be rigorous."

Category 3: Multi-Step Word Problems

Six word problems requiring multiple arithmetic or algebraic steps, including two intentional "trick" questions designed to catch models that pattern-match instead of reason.

The Results

Logic Puzzles (5 problems, scored 0-5)

Mathematical Proofs (4 proofs, scored 0-4)

Multi-Step Word Problems (6 problems, scored 0-6)

Overall Rankings

Rankings are ordered by combined score, with math proof quality (rigor and completeness) as the tiebreaker for equal scores.

DeepSeek R1 ties with GPT-5 on raw score but at a quarter of the cost. Llama 4 Maverick is the cheapest option but shows the gap between open-source and frontier reasoning.

Key Takeaways

Dedicated reasoning models are worth the wait

The o4-mini model was the only one to score perfectly across all three categories. It takes longer per request -- often 3-4x slower than GPT-5 -- but if correctness matters more than latency, the tradeoff is clear. The reasoning_effort parameter lets you dial this down when you need faster results.

Chain-of-thought prompting still matters

Every model performed better when explicitly asked to show its reasoning. Even models with built-in reasoning (o4-mini, DeepSeek R1) benefited from the structured system prompt. If you are not including "think step by step" or similar instructions, you are leaving accuracy on the table.

Cost and accuracy do not always correlate

DeepSeek R1 delivered GPT-5-level accuracy at roughly 25% of the cost. For batch processing of reasoning tasks -- grading, classification, data validation -- that cost efficiency compounds into significant savings.

Run Your Own Reasoning Benchmarks

These rankings reflect general reasoning ability, but your domain-specific problems may tell a different story. A model that aces abstract logic might struggle with your particular flavor of financial modeling or legal reasoning.

Head to Promptster and test with your actual prompts. Select multiple providers, run the same reasoning task across all of them, and use the evaluation scoring to quantify which model handles your workload best. You can save the results and track performance over time with scheduled tests -- so you will know immediately if a model update changes the math.