Kimi vs Qwen for Math
Kimi has a notable edge for pure mathematics, particularly on the AIME 2025 benchmark where it scores 96.1% versus Qwen's 91.3%—a significant gap for competitive math problems. Both models support extended thinking for complex reasoning, but Kimi's stronger performance on math-specific tasks makes it the better choice if advanced mathematical problem-solving is your priority. Qwen remains competitive for general reasoning and is more cost-effective, making it a solid alternative if pricing and multilingual capabilities matter more than peak math performance.
Head-to-Head for Math
| Criteria | Kimi | Qwen | Winner |
|---|---|---|---|
| Competition-level mathematics (AIME 2025) | Exceptional performance at 96.1% | Strong performance at 91.3% | Kimi |
| Advanced STEM knowledge (GPQA Diamond) | Solid performance at 87.6% | Stronger edge at 88.4% | Qwen |
| Reasoning depth for complex proofs | Strong competitive reasoning capabilities | Extended thinking supported | Kimi |
| Context window for lengthy problems | 128K token context | 256K token context | Qwen |
| Cost efficiency for frequent use | $0.60 per 1M input tokens | $0.40 per 1M input tokens | Qwen |
Competition-level mathematics (AIME 2025)
Kimi
Exceptional performance at 96.1%
Qwen
Strong performance at 91.3%
Advanced STEM knowledge (GPQA Diamond)
Kimi
Solid performance at 87.6%
Qwen
Stronger edge at 88.4%
Reasoning depth for complex proofs
Kimi
Strong competitive reasoning capabilities
Qwen
Extended thinking supported
Context window for lengthy problems
Kimi
128K token context
Qwen
256K token context
Cost efficiency for frequent use
Kimi
$0.60 per 1M input tokens
Qwen
$0.40 per 1M input tokens
See Them Answer a Math Question
We asked both models the same math-focused prompt.
“Solve the integral ∫(x²·ln(x))dx step by step, then verify your answer by differentiating the result.”
Solve the integral ∫(x²·ln(x))dx step by step, then verify your answer by differentiating the result.
Here is the step-by-step solution using integration by parts.
Step 1: Choose $u$ and $dv$
For $\int x^2 \ln(x) , dx$, we use the integration by parts formula:
We choose:
- $u = \ln(x)$ (since it simplifies when differentiated)
- $dv = x^2 , dx$ (since it's easy to integrate)
This gives us:
- $du = \frac{1}{x} , dx$
- $v = \frac{x^3}{3}$
Step 2: Apply the formula
Step 3: Simplify the remaining integral
Step 4: Final Answer
(Or factored form: $\frac{x^3}{9}(3\ln(x) - 1) + C$)
Verification by Differentiation
Let's differentiate $F(x) = \frac{x^3 \ln(x)}{3} - \frac{x^3}{9} + C$:
Term 1: $\frac{d}{dx}\left[\frac{x^3 \ln(x)}{3}\right]$ Using the product rule on $x^3 \ln(x)$:
Term 2: $\frac{d}{dx}\left[-\frac{x^3}{9}\right] = -\frac{3x^2}{9} = -\frac{x^2}{3}$
Term 3: $\frac{d}{dx}[C] = 0$
Adding together:
Since $\frac{d}{dx}\left[\frac{x^3 \ln(x)}{3} - \frac{x^3}{9} + C\right] = x^2 \ln(x)$, the solution is verified.
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Detailed Breakdown
Kimi and Qwen are both strong contenders for mathematical work, but the benchmarks tell a clear story: Kimi has a notable edge when it comes to pure math performance. On AIME 2025, a rigorous competition math benchmark, Kimi scores 96.1% compared to Qwen's 91.3% — a meaningful gap that reflects Kimi's particular strength in multi-step mathematical reasoning. Kimi also leads on Humanity's Last Exam (30.1% vs 28.7%), a benchmark that includes advanced graduate-level math and science problems. Qwen edges ahead on GPQA Diamond (88.4% vs 87.6%) and MMLU Pro (87.8% vs 87.1%), suggesting stronger breadth across science and applied reasoning, but Kimi's AIME advantage signals that for intensive symbolic math and proof-style problems, it's the sharper tool.
For students and researchers tackling olympiad-style problems, calculus proofs, linear algebra, or number theory, Kimi's reasoning engine tends to work through multi-step derivations with greater precision. Its parallel sub-task coordination also helps when a problem requires breaking into independent components — useful for things like system-of-equations setups or multi-variable optimization.
Qwen holds its own for applied and practical math. Its 256K context window is a real advantage when working through long technical documents, research papers, or extended problem sets where you need the model to hold a lot of context simultaneously. If you're doing data analysis, statistics-heavy work, or need to cross-reference large tables of numerical data, Qwen's larger context is genuinely useful. It also performs well across multilingual math content, making it the better choice if you're working with non-English math textbooks or documentation.
Both models offer extended thinking, which is critical for math — the ability to reason step-by-step before producing an answer significantly reduces careless errors. Neither supports code execution natively, which is worth noting: for computational math or numerical methods, you'll still want a tool like Python alongside either model.
On pricing, Qwen is slightly cheaper ($0.40/1M input tokens vs Kimi's $0.60), which matters if you're running high-volume math tutoring pipelines or batch-processing problem sets via API.
Recommendation: For pure mathematical reasoning — competition math, proofs, symbolic manipulation — choose Kimi. Its AIME 2025 score is hard to argue with, and it's built for exactly the kind of deep, sequential reasoning that hard math demands. For applied math within larger technical documents, multilingual contexts, or cost-sensitive deployments, Qwen is a capable and practical alternative.
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