| Signal | Kimi K2.5 | Delta | QwQ 32B |
|---|---|---|---|
Capabilities | 83 | +33 | |
Benchmarks | 57 | -1 | |
Pricing | 98 | -1 | |
Context window size | 86 | +5 | |
Recency | 100 | +40 | |
Output Capacity | 90 | +5 | |
| Overall Result | 4 wins | of 6 | 2 wins |
Score History
59.1
current score
Kimi K2.5
right now
58.9
current score
Moonshot AI
Alibaba
QwQ 32B saves you $80.27/month
That's $963.24/year compared to Kimi K2.5 at your current usage level of 100K calls/month.
| Metric | Kimi K2.5 | QwQ 32B | Winner |
|---|---|---|---|
| Overall Score | 59 | 59 | Kimi K2.5 |
| Rank | #134 | #135 | Kimi K2.5 |
| Quality Rank | #134 | #135 | Kimi K2.5 |
| Adoption Rank | #134 | #135 | Kimi K2.5 |
| Parameters | -- | 32B | -- |
| Context Window | 262K | 131K | Kimi K2.5 |
| Pricing | $0.38/$1.72/M | $0.15/$0.58/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 50 | Kimi K2.5 |
| Benchmarks | 57 | 58 | QwQ 32B |
| Pricing | 98 | 99 | QwQ 32B |
| Context window size | 86 | 81 | Kimi K2.5 |
| Recency | 100 | 61 | Kimi K2.5 |
| Output Capacity | 90 | 85 | Kimi K2.5 |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 59/100 (rank #134), placing it in the top 54% of all 290 models tracked.
Scores 59/100 (rank #135), placing it in the top 54% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
QwQ 32B offers 65% better value per quality point. At 1M tokens/day, you'd spend $10.95/month with QwQ 32B vs $31.54/month with Kimi K2.5 - a $20.59 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. QwQ 32B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.58/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (59/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Kimi K2.5 and QwQ 32B are extremely close in overall performance (only 0.20000000000000284 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Kimi K2.5
Marginally better benchmark scores; both are excellent
Best for Cost
QwQ 32B
65% lower pricing; better value at scale
Best for Reliability
Kimi K2.5
Higher uptime and faster response speeds
Best for Prototyping
Kimi K2.5
Stronger community support and better developer experience
Best for Production
Kimi K2.5
Wider enterprise adoption and proven at scale
by Moonshot AI
| Capability | Kimi K2.5 | QwQ 32B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Moonshot AI
Alibaba
QwQ 32B saves you $1.79/month
That's 65% cheaper than Kimi K2.5 at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Kimi K2.5 | QwQ 32B |
|---|---|---|
| Context Window | 262K | 131K |
| Max Output Tokens | 262,144 | 131,072 |
| Open Source | Yes | Yes |
| Created | Jan 27, 2026 | Mar 5, 2025 |
Kimi K2.5 scores 59/100 (rank #134) compared to QwQ 32B's 59/100 (rank #135), giving it a 0-point advantage. Kimi K2.5 is the stronger overall choice, though QwQ 32B may excel in specific areas like cost efficiency.
Kimi K2.5 is ranked #134 and QwQ 32B is ranked #135 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
QwQ 32B is cheaper at $0.58/M output tokens vs Kimi K2.5's $1.72/M output tokens - 3.0x more expensive. Input token pricing: Kimi K2.5 at $0.38/M vs QwQ 32B at $0.15/M.
Kimi K2.5 has a larger context window of 262,144 tokens compared to QwQ 32B's 131,072 tokens. A larger context window means the model can process longer documents and conversations.