DeepSeek vs Qwen for Math
DeepSeek edges out Qwen on standardized math competitions (AIME: 93.1% vs 91.3%), but Qwen demonstrates stronger performance across broader math benchmarks like GPQA Diamond (88.4% vs 82.4%) and MMLU Pro (87.8% vs 85.0%). For general mathematical problem-solving, Qwen is the more well-rounded choice, while DeepSeek excels specifically at contest-style math with its dedicated R1 reasoning mode—both are highly capable and affordable options.
Head-to-Head for Math
| Criteria | DeepSeek | Qwen | Winner |
|---|---|---|---|
| Math Competition Performance | Outstanding (93.1% AIME) | Very strong (91.3% AIME) | DeepSeek |
| Quantitative Reasoning | Strong (82.4% GPQA) | Excellent (88.4% GPQA) | Qwen |
| Extended Reasoning Capability | Yes (R1 reasoning mode) | Yes (extended thinking) | Tie |
| Context for Long Mathematical Proofs | Adequate (128K tokens) | Superior (256K tokens) | Qwen |
| API Cost for Math Solutions | More economical (cheaper output) | Higher output costs | DeepSeek |
Math Competition Performance
DeepSeek
Outstanding (93.1% AIME)
Qwen
Very strong (91.3% AIME)
Quantitative Reasoning
DeepSeek
Strong (82.4% GPQA)
Qwen
Excellent (88.4% GPQA)
Extended Reasoning Capability
DeepSeek
Yes (R1 reasoning mode)
Qwen
Yes (extended thinking)
Context for Long Mathematical Proofs
DeepSeek
Adequate (128K tokens)
Qwen
Superior (256K tokens)
API Cost for Math Solutions
DeepSeek
More economical (cheaper output)
Qwen
Higher output costs
See Them Answer a Math Question
We asked both models the same math-focused prompt.
“Solve this step-by-step: Find all integer solutions to the equation x³ - 6x² + 11x - 6 = 0, then prove that the sum of cubes of the roots equals 36.”
Solve this step-by-step: Find all integer solutions to the equation x³ - 6x² + 11x - 6 = 0, then prove that the sum of cubes of the roots equals 36.
Alright, let's work through this step-by-step.
Step 1 – Solve for integer solutions.
We look for integer roots using the Rational Root Theorem: possible integer roots are divisors of the constant term : .
Try : Yes, is a root.
Now perform polynomial division: divided by :
Using synthetic division with :
So the quotient is .
Factor : Roots: and .
Thus, integer solutions: .
Step 2 – Sum of cubes of the roots
The roots are .
We want :
Numerically, it's already .
Step 3 – Prove algebraically using Vieta's formulas
For , sum of roots .
Sum of roots taken two at a time: .
Product of roots: .
We know the identity: Let’s verify the identity first (standard identity for sum of cubes of roots in terms of elementary symmetric sums).
Actually, known formula: Where , , .
Check:
From , but better:
Use (since etc., sum over all symmetric).
Derivation (known identity): Rearrange:
Step 4 – Plug values
Here:
Step 5 – Conclusion
We found integer solutions , and using Vieta’s formulas we proved the sum of their cubes is .
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Detailed Breakdown
Both DeepSeek and Qwen are serious contenders for mathematical work, and the benchmarks tell an interesting story. DeepSeek scores 93.1% on AIME 2025 — a competition-level math benchmark — edging out Qwen's 91.3%. That gap, while small, is meaningful: AIME problems require multi-step algebraic and combinatorial reasoning, so DeepSeek's advantage here reflects genuine strength in structured mathematical problem-solving.
DeepSeek's edge in pure math stems partly from its R1 reasoning model, which uses extended chain-of-thought thinking to work through proofs, calculus problems, and number theory step by step. For tasks like solving differential equations, verifying mathematical proofs, or tackling olympiad-style problems, DeepSeek R1 is one of the strongest open-source options available. Students preparing for competitions or researchers working through formal derivations will find DeepSeek particularly capable.
Qwen closes the gap quickly when math intersects with broader knowledge domains. Its MMLU Pro score of 87.8% (versus DeepSeek's 85.0%) and GPQA Diamond score of 88.4% (versus 82.4%) suggest Qwen performs better on applied and interdisciplinary problems — think physics word problems, statistics in data science contexts, or financial mathematics. Qwen's image understanding capability is also a practical differentiator: you can photograph a handwritten equation or a textbook page and have Qwen parse and solve it directly. DeepSeek cannot do this.
For classroom or tutoring use cases, Qwen's 256K context window gives it a significant advantage. It can hold an entire problem set, worked solutions, and ongoing student dialogue in a single session without losing context. DeepSeek's 128K window is still generous but may hit limits in lengthy tutoring sessions or when working through a full chapter of problems.
Both models are highly cost-effective compared to commercial alternatives, with API pricing under $2 per million output tokens. DeepSeek is slightly cheaper on input ($0.56/M vs $0.40/M favoring Qwen, though Qwen charges more on output), making either viable for high-volume math tutoring applications.
The recommendation depends on the use case. For pure mathematical reasoning — proofs, competition problems, symbolic manipulation — DeepSeek with its R1 model is the stronger choice. For applied math, STEM problem-solving that involves diagrams or images, or long-form tutoring sessions, Qwen's broader capabilities and larger context window give it the edge. Power users doing serious mathematical work would do well to keep both accessible and route tasks accordingly.
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