If you care about reasoning depth
Start with GPT-5 when quality and reliability matter most for this use-case.
Use-case Guide
Best LLM for Math (2026)
Top models for quantitative reasoning, proofs, and step-by-step problem solving.
Last updated: February 27, 2026
Overview
Math-focused workflows need LLMs that show reliable step-by-step reasoning, symbolic consistency, and low hallucination rates.
Editorial summary
For math use-cases, we evaluate symbolic accuracy, consistency across steps, and reliability under multi-part reasoning prompts.
How we evaluate models for this use-case
Rankings reflect numerical accuracy, step consistency, and reliability under multi-step reasoning. We prioritize models that maintain quality consistently for math workflows.
Evaluation checklist
- Use fixed benchmark questions with known answers.
- Evaluate intermediate reasoning consistency.
- Check failure behavior under ambiguous inputs.
- Validate output against deterministic calculators when possible.
Common pitfalls
- Trusting final answers without checking intermediate steps.
- Ignoring drift across repeated runs.
- Mixing outdated market assumptions into prompts.
Top picks
| Rank | Model | Vendor | Actions |
|---|---|---|---|
| #1 | GPT-5 | OpenAI | |
| #2 | Kimi | Moonshot AI | |
| #3 | DeepSeek V3/R1 Family | DeepSeek | |
| #4 | Qwen2.x Family | Alibaba | |
| #5 | Gemini | ||
| #6 | Claude | Anthropic | |
| #7 | OpenAI o-series | OpenAI | |
| #8 | GPT-4.1 | OpenAI | |
| #9 | GPT-4o | OpenAI | |
| #10 | Gemini 1.5/2.x Family | ||
| #11 | GLM / ChatGLM / GLM-4 Family | Zhipu AI | |
| #12 | Mistral Large | Mistral AI | |
| #13 | Claude 3.5/3.7/4 Family | Anthropic | |
| #14 | Llama 3/4 Family | Meta | |
| #15 | Mixtral | Mistral AI | |
| #16 | Command R / R+ | Cohere | |
| #17 | Jurassic Family | AI21 | |
| #18 | Hunyuan | Tencent | |
| #19 | Doubao | ByteDance | |
| #20 | abab / MiniMax Family | MiniMax | |
| #21 | Baichuan | Baichuan | |
| #22 | Grok | xAI | |
| #23 | Jamba | AI21 | |
| #24 | Nova Family | Amazon | |
| #25 | ERNIE | Baidu |
Decision blocks
If you care about response latency
Use GPT-4o for faster cycles and throughput.
Detailed model breakdown
#1 GPT-5 (OpenAI)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong coding and refactoring quality
- Good multi-file reasoning
- Useful for architecture decisions
Cons
- Can be expensive at scale
- May over-engineer simple tasks
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Premium model pricing; best for high-value engineering tasks.
#2 Kimi (Moonshot AI)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong long-context capabilities
- Good Chinese-language performance
- Competitive reasoning quality
Cons
- Availability and integration vary by region
- Needs governance checks for global deployments
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Popular in East-Asia focused evaluation sets.
#3 DeepSeek V3/R1 Family (DeepSeek)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong reasoning and coding potential
- Competitive performance in many benchmarks
- Good cost-performance interest
Cons
- Requires strict evaluation for production safety
- Operational maturity depends on deployment setup
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Commonly tested for high-value reasoning and coding workloads.
#4 Qwen2.x Family (Alibaba)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Broad model range across sizes
- Strong multilingual support
- Good open and commercial ecosystem options
Cons
- Variant selection can be complex
- Quality differs by size and tuning
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Widely benchmarked for both enterprise and open deployment scenarios.
#5 Gemini (Google)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Fast responses in iterative workflows
- Solid quantitative reasoning
- Good ecosystem integration
Cons
- Consistency can vary by prompt style
- Needs validation for critical calculations
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Often competitive on speed-oriented workloads.
#6 Claude (Anthropic)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Clear technical writing and reasoning
- Strong for long-context code analysis
- Good step-by-step math explanations
Cons
- Can be conservative in edge-case assumptions
- Output style may require prompt tuning
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Balanced performance-cost profile for many team workflows.
#7 OpenAI o-series (OpenAI)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong reasoning-focused capability
- Useful for complex multi-step tasks
- Good for high-stakes analysis
Cons
- Can be slower on heavy prompts
- Cost profile should be benchmarked for scale
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Reasoning-focused family; best for tasks where depth matters.
#8 GPT-4.1 (OpenAI)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong general reasoning
- Good coding and analysis quality
- Reliable for enterprise workflows
Cons
- Premium pricing in high-volume usage
- Needs evaluation per use-case
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Enterprise-oriented pricing; evaluate based on workload scale.
#9 GPT-4o (OpenAI)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Fast responses
- Strong multimodal support
- Good quality-speed balance
Cons
- Output depth can vary by prompt
- May require structured prompting for stability
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Often used where balanced speed and quality are required.
#10 Gemini 1.5/2.x Family (Google)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Good performance across broad tasks
- Competitive speed in many scenarios
- Works well in Google ecosystem workflows
Cons
- Output consistency can vary by prompt style
- Needs benchmark validation per task class
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Often chosen for mixed workloads requiring speed and breadth.
#11 GLM / ChatGLM / GLM-4 Family (Zhipu AI)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong Chinese-language utility
- Growing ecosystem support
- Useful enterprise model lineup
Cons
- Global integration can vary by region
- Needs use-case specific validation
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Frequently included in East-Asia enterprise model evaluations.
#12 Mistral Large (Mistral AI)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong multilingual capability
- Good enterprise quality
- Fast iterative usage
Cons
- Needs workload-specific benchmarking
- Feature parity depends on deployment context
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Commonly evaluated for enterprise productivity and multilingual use.
#13 Claude 3.5/3.7/4 Family (Anthropic)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Clear writing and long-context handling
- Strong quality in complex drafting tasks
- Reliable instruction following
Cons
- Conservative style for some creative tasks
- Needs prompt tuning for tone control
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Balanced for quality-sensitive workflows and long-context use.
#14 Llama 3/4 Family (Meta)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Flexible deployment options
- Strong open ecosystem support
- Good for customization and self-hosting
Cons
- Operational overhead for self-managed setups
- Quality varies across model variants
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Attractive for teams prioritizing control and custom deployment.
#15 Mixtral (Mistral AI)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Efficient Mixture-of-Experts architecture
- Strong open model ecosystem
- Good cost-performance potential
Cons
- Infrastructure tuning may be needed
- Quality can vary by variant and hosting stack
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Often used where open deployment flexibility is important.
#16 Command R / R+ (Cohere)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong retrieval-augmented workflows
- Good enterprise integration focus
- Useful for business knowledge tasks
Cons
- Performance depends on retrieval stack quality
- Needs tuning for domain precision
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Frequently used in enterprise RAG and support-oriented systems.
#17 Jurassic Family (AI21)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Broad language generation coverage
- Useful for drafting workflows
- Established model family
Cons
- Newer alternatives may outperform on some tasks
- Needs domain-specific evaluation
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Legacy-to-modern transition use-cases should benchmark carefully.
#18 Hunyuan (Tencent)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong platform integration options
- Useful for broad assistant workloads
- Good ecosystem leverage
Cons
- Output quality depends on variant and prompt design
- Needs production benchmark validation
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Often chosen where Tencent ecosystem alignment is important.
#19 Doubao (ByteDance)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Fast interaction patterns
- Useful for high-throughput scenarios
- Strong productization focus
Cons
- Needs strict quality controls for critical workflows
- Integration options vary by region
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Commonly tested for scalable user-facing assistant flows.
#20 abab / MiniMax Family (MiniMax)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Broad multimodal ambitions
- Strong consumer-scale product focus
- Useful regional ecosystem options
Cons
- Task-level quality varies across model variants
- Requires careful enterprise benchmarking
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Often assessed for product-facing conversational workloads.
#21 Baichuan (Baichuan)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Useful open and enterprise model options
- Good multilingual potential
- Strong candidate for model diversity
Cons
- Quality can vary by release and tuning
- Requires practical benchmarking
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Included frequently in broad East/West comparison matrices.
#22 Grok (xAI)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Fast conversational iteration
- Useful for exploration workflows
- Strong real-time style responses
Cons
- Requires rigorous validation in critical domains
- Output style may need constraints
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Evaluate primarily for exploration and rapid ideation workloads.
#23 Jamba (AI21)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Hybrid architecture strengths
- Good long-context utility
- Practical for mixed business tasks
Cons
- Requires benchmark comparison against alternatives
- Integration maturity varies by stack
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Evaluate for long-context workflows and enterprise reasoning tasks.
#24 Nova Family (Amazon)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Cloud-native integration potential
- Useful for enterprise deployment paths
- Good operational ecosystem alignment
Cons
- Performance depends on model variant selection
- Requires workload-level benchmarking
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Often evaluated by teams already aligned with AWS stacks.
#25 ERNIE (Baidu)
What it's best at for Math: step-by-step quantitative reasoning and symbolic consistency.
Pros
- Strong regional ecosystem integration
- Useful for Chinese-language enterprise workflows
- Good applied AI tooling support
Cons
- Cross-region availability can vary
- Requires benchmark checks for global use-cases
Who should choose it: teams using LLMs for math workflows that require repeatable quality and human oversight.
Pricing notes: Best assessed in region-aligned enterprise stacks.
Frequently asked questions
Are LLMs reliable for advanced math?
They are useful, but outputs should be verified with independent checks for high-stakes or advanced calculations.
How should I evaluate a math LLM?
Use a fixed benchmark of representative problems, then compare error rate, reasoning clarity, and consistency across retries.