Model Comparison
Kimi K2-Thinking-0905 vs Qwen3 VL 235B A22B InstructWhich is better in 2026?
Kimi K2-Thinking-0905 significantly outperforms across most benchmarks. Qwen3 VL 235B A22B Instruct is 1.4x cheaper per token.
Verdict: Kimi K2-Thinking-0905 vs Qwen3 VL 235B A22B Instruct — which is better?
Kimi K2-Thinking-0905 (by Moonshot AI) and Qwen3 VL 235B A22B Instruct (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
Kimi K2-Thinking-0905 outperforms in 4 benchmarks (AIME 2025, LiveCodeBench v6, MMLU-Pro, MMLU-Redux), while Qwen3 VL 235B A22B Instruct is better at 1 benchmark (WritingBench). Kimi K2-Thinking-0905 significantly outperforms across most benchmarks.
On price, Qwen3 VL 235B A22B Instruct is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Choose Kimi K2-Thinking-0905 if…
- you want the strongest raw capability — it leads on 4 of 5 shared benchmarks
Choose Qwen3 VL 235B A22B Instruct if…
- cost matters — it's about 1.4x cheaper per token
- you want the most recent training data — it shipped Sep 2025
Performance Benchmarks
Comparative analysis across standard metrics
Kimi K2-Thinking-0905 outperforms in 4 benchmarks (AIME 2025, LiveCodeBench v6, MMLU-Pro, MMLU-Redux), while Qwen3 VL 235B A22B Instruct is better at 1 benchmark (WritingBench).
Kimi K2-Thinking-0905 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Kimi K2-Thinking-0905 ($0.47/1M tokens) is 1.6x more expensive than Qwen3 VL 235B A22B Instruct ($0.30/1M tokens).
For output processing, Kimi K2-Thinking-0905 ($2.00/1M tokens) is 1.3x more expensive than Qwen3 VL 235B A22B Instruct ($1.49/1M tokens).
In conclusion, Kimi K2-Thinking-0905 is more expensive than Qwen3 VL 235B A22B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2-Thinking-0905 has 764.0B more parameters than Qwen3 VL 235B A22B Instruct, making it 323.7% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 262,144 tokens. Both models can generate responses up to 262,144 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 235B A22B Instruct supports multimodal inputs, whereas Kimi K2-Thinking-0905 does not.
Qwen3 VL 235B A22B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2-Thinking-0905
Qwen3 VL 235B A22B Instruct
License
Usage and distribution terms
Kimi K2-Thinking-0905 is licensed under MIT, while Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct was released on 2025-09-22.
Qwen3 VL 235B A22B Instruct is 1 month newer than Kimi K2-Thinking-0905.
Sep 5, 2025
9 months ago
Sep 22, 2025
8 months ago
2w newerKnowledge 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 VL 235B A22B Instruct is available from DeepInfra, Novita.
Kimi K2-Thinking-0905
Qwen3 VL 235B A22B Instruct
Outputs Comparison
Key Takeaways
Kimi K2-Thinking-0905
View detailsMoonshot AI
Qwen3 VL 235B A22B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about Kimi K2-Thinking-0905 vs Qwen3 VL 235B A22B Instruct.