Best AI Models for Math & Reasoning (2026)

Mathematical reasoning is where AI models differ most. The gap between the best and worst models on math benchmarks is larger than on any other task category. Here’s how they rank.

Last verified: March 2026

Rankings

Rank	Model	MATH Score	Best For
🥇	Gemini 3.1 Deep Think	92.1%	Competition math, proofs
🥈	GPT-5.4 Thinking	88.7%	Applied math, speed
🥉	Claude Opus 4.6 (extended)	87.3%	Mathematical coding
4	DeepSeek R1	86.8%	Open-source, self-hosted
5	Kimi K2.5	85.6%	Free, multimodal math
6	Llama 4	82.4%	Local deployment

Gemini 3.1 Deep Think — The Math Champion

Google’s Deep Think mode is purpose-built for mathematical reasoning. It allocates extended compute to decompose problems into steps, verify each step, and backtrack when it hits dead ends.

What it excels at:

• Multi-step algebraic proofs
• Calculus and real analysis
• Competition math (AMC, AIME, Putnam)
• Number theory
• Combinatorics

Benchmark performance:

Test	Gemini Deep Think	GPT-5.4	Claude 4.6
MATH	92.1%	88.7%	87.3%
AMC 12	~85%	~78%	~76%
AIME	~60%	~48%	~45%
GSM8K	97.8%	96.2%	95.9%

The Deep Think advantage is most pronounced on harder problems. On GSM8K (grade school math), all models perform similarly. On AIME (competition math), Gemini pulls significantly ahead.

GPT-5.4 Thinking — Best Speed-to-Accuracy

GPT-5.4’s Thinking mode offers a strong balance between mathematical accuracy and response speed. It’s notably faster than Gemini Deep Think while still achieving strong scores.

Best for:

• Applied mathematics and statistics
• Quick calculations with verification
• Data science and ML math
• Financial modeling

Claude Opus 4.6 — Best for Mathematical Code

Claude Opus 4.6 with extended thinking (16K budget) is the best choice when you need to both reason about math AND implement it in code. Its SWE-Bench dominance extends to scientific computing libraries.

Best for:

• Implementing numerical algorithms
• Scientific computing (NumPy, SciPy, Julia)
• Mathematical proof verification code
• Statistical analysis pipelines

# Claude excels at mathematical code like this
def newton_raphson(f, df, x0, tol=1e-12, max_iter=100):
    """Find root of f using Newton-Raphson with convergence checks."""
    x = x0
    for i in range(max_iter):
        fx, dfx = f(x), df(x)
        if abs(dfx) < 1e-15:
            raise ValueError(f"Derivative near zero at x={x}")
        x_new = x - fx / dfx
        if abs(x_new - x) < tol:
            return x_new, i + 1
        x = x_new
    raise ValueError(f"No convergence after {max_iter} iterations")

DeepSeek R1 — Best Open-Source for Math

DeepSeek R1’s chain-of-thought reasoning was designed from the ground up for mathematical problems. At 86.8% on MATH, it’s the strongest open-source option for mathematical reasoning.

Advantages:

• Free to self-host
• Strong chain-of-thought reasoning
• Competitive with proprietary models
• Can be fine-tuned on domain-specific math

Choosing the Right Model

Use case	Best model
Competition math	Gemini 3.1 Deep Think
Quick calculations	GPT-5.4 (speed)
Math + code together	Claude Opus 4.6
Self-hosted	DeepSeek R1
Budget-friendly	Kimi K2.5 (free)
Education/tutoring	GPT-5.4 (clearest explanations)
Research proofs	Gemini 3.1 Deep Think

For most developers, GPT-5.4 or Claude Opus 4.6 covers mathematical needs alongside other coding tasks. Gemini 3.1 Deep Think is the specialist pick when math accuracy is the top priority.

Last verified: March 2026