Kimi K2-Thinking-0905 vs Qwen3-Coder
Comparing Kimi K2-Thinking-0905 and Qwen3-Coder across benchmarks, pricing, and capabilities.
Moonshot AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Kimi K2-Thinking-0905 and Qwen3-Coder trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Qwen3-Coder is roughly 4.7x 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
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
Choose Qwen3-Coder
- cost matters — it's about 4.7x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
21 reported for Kimi K2-Thinking-0905 · 0 for Qwen3-Coder
Kimi K2-Thinking-0905 and Qwen3-Coderdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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 2.6x more expensive than Qwen3-Coder ($0.18/1M tokens).
For output processing, Kimi K2-Thinking-0905 ($2.00/1M tokens) is 11.1x more expensive than Qwen3-Coder ($0.18/1M tokens).
In conclusion, Kimi K2-Thinking-0905 is more expensive than Qwen3-Coder.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2-Thinking-0905 has 520.0B more parameters than Qwen3-Coder, making it 108.3% larger.
Context Window
Maximum input and output token capacity
Kimi K2-Thinking-0905 accepts 262,144 input tokens compared to Qwen3-Coder's 256,000 tokens. Kimi K2-Thinking-0905 can generate longer responses up to 262,144 tokens, while Qwen3-Coder is limited to 256,000 tokens.
License
Usage and distribution terms
Kimi K2-Thinking-0905 is licensed under MIT, while Qwen3-Coder uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Kimi K2-Thinking-0905 was released on 2025-09-05, while Qwen3-Coder was released on 2025-01-01.
Kimi K2-Thinking-0905 is 8 months newer than Qwen3-Coder.
Sep 5, 2025
1.0 years ago
8mo newerJan 1, 2025
1.7 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. Qwen3-Coder is available from DeepInfra, Fireworks.
Kimi K2-Thinking-0905
Qwen3-Coder
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2-Thinking-0905 and Qwen3-Coder side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2-Thinking-0905 vs Qwen3-Coder.