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

Competition-level mathematics (AIME 2025)

Kimi

Kimi

Exceptional performance at 96.1%

Qwen

Strong performance at 91.3%

Advanced STEM knowledge (GPQA Diamond)

Qwen

Kimi

Solid performance at 87.6%

Qwen

Stronger edge at 88.4%

Reasoning depth for complex proofs

Kimi

Kimi

Strong competitive reasoning capabilities

Qwen

Extended thinking supported

Context window for lengthy problems

Qwen

Kimi

128K token context

Qwen

256K token context

Cost efficiency for frequent use

Qwen

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.

Kimi K2.5

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: udv=uvvdu\int u \, dv = uv - \int v \, du

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

x2ln(x)dx=uvvdu\int x^2 \ln(x) \, dx = uv - \int v \, du =ln(x)x33x331xdx= \ln(x) \cdot \frac{x^3}{3} - \int \frac{x^3}{3} \cdot \frac{1}{x} \, dx

Step 3: Simplify the remaining integral

=x3ln(x)3x23dx= \frac{x^3 \ln(x)}{3} - \int \frac{x^2}{3} \, dx =x3ln(x)313x2dx= \frac{x^3 \ln(x)}{3} - \frac{1}{3} \int x^2 \, dx =x3ln(x)313x33+C= \frac{x^3 \ln(x)}{3} - \frac{1}{3} \cdot \frac{x^3}{3} + C

Step 4: Final Answer

x2ln(x)dx=x3ln(x)3x39+C\boxed{\int x^2 \ln(x) \, dx = \frac{x^3 \ln(x)}{3} - \frac{x^3}{9} + C}

(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 $x^3 \ln(x)$: =13[3x2ln(x)+x31x]=13[3x2ln(x)+x2]=x2ln(x)+x23= \frac{1}{3}\left[3x^2 \cdot \ln(x) + x^3 \cdot \frac{1}{x}\right] = \frac{1}{3}\left[3x^2 \ln(x) + x^2\right] = x^2 \ln(x) + \frac{x^2}{3}

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: (x2ln(x)+x23)+(x23)+0=x2ln(x)\left(x^2 \ln(x) + \frac{x^2}{3}\right) + \left(-\frac{x^2}{3}\right) + 0 = x^2 \ln(x)

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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