Kimi K2-Thinking-0905 vs Qwen2.5 72B Instruct
Kimi K2-Thinking-0905 leads the LLM Stats Score 36.0 to 12.1. Qwen2.5 72B Instruct is 2.4x cheaper per token.
Moonshot AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Kimi K2-Thinking-0905 leads the overall LLM Stats Score 36.0 to 12.1, ranking #81 overall.
In the 3 individual benchmarks reported for both models, Kimi K2-Thinking-0905 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen2.5 72B Instruct is roughly 2.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2-Thinking-0905 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Kimi K2-Thinking-0905
- overall performance matters — it scores 36.0 and ranks #81 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
Choose Qwen2.5 72B Instruct
- cost matters — it's about 2.4x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
21 reported for Kimi K2-Thinking-0905 · 14 for Qwen2.5 72B Instruct
Kimi K2-Thinking-0905 outperforms in 3 benchmarks (GPQA, MMLU-Pro, MMLU-Redux), while Qwen2.5 72B Instruct is better at 0 benchmarks.
Kimi K2-Thinking-0905 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Kimi K2-Thinking-0905 ($0.47/1M tokens) is 1.3x more expensive than Qwen2.5 72B Instruct ($0.35/1M tokens).
For output processing, Kimi K2-Thinking-0905 ($2.00/1M tokens) is 5.0x more expensive than Qwen2.5 72B Instruct ($0.40/1M tokens).
In conclusion, Kimi K2-Thinking-0905 is more expensive than Qwen2.5 72B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2-Thinking-0905 has 927.3B more parameters than Qwen2.5 72B Instruct, making it 1275.5% larger.
Context Window
Maximum input and output token capacity
Kimi K2-Thinking-0905 accepts 262,144 input tokens compared to Qwen2.5 72B Instruct's 131,072 tokens. Kimi K2-Thinking-0905 can generate longer responses up to 262,144 tokens, while Qwen2.5 72B Instruct is limited to 8,192 tokens.
License
Usage and distribution terms
Kimi K2-Thinking-0905 is licensed under MIT, while Qwen2.5 72B Instruct uses Qwen.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Qwen
Open weights
Release Timeline
When each model was launched
Kimi K2-Thinking-0905 was released on 2025-09-05, while Qwen2.5 72B Instruct was released on 2024-09-19.
Kimi K2-Thinking-0905 is 12 months newer than Qwen2.5 72B Instruct.
Sep 5, 2025
1.0 years ago
11mo newerSep 19, 2024
2.0 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Kimi K2-Thinking-0905 is available from DeepInfra, Novita, Fireworks. Qwen2.5 72B Instruct is available from DeepInfra, Hyperbolic, Fireworks, Together.
Kimi K2-Thinking-0905
Qwen2.5 72B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2-Thinking-0905 and Qwen2.5 72B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2-Thinking-0905 vs Qwen2.5 72B Instruct.