Kimi K2-Thinking-0905 vs Qwen3 VL 4B Instruct
Kimi K2-Thinking-0905 leads the LLM Stats Score 36.0 to 10.7. Qwen3 VL 4B Instruct is 3.8x 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 10.7, ranking #81 overall.
In the 5 individual benchmarks reported for both models, Kimi K2-Thinking-0905 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3 VL 4B Instruct is roughly 3.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
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 and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 5 exact shared results
Choose Qwen3 VL 4B Instruct
- cost matters — it's about 3.8x cheaper per token
- you want the most recent training data — it shipped Sep 2025
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 · 45 for Qwen3 VL 4B Instruct
Kimi K2-Thinking-0905 outperforms in 4 benchmarks (AIME 2025, LiveCodeBench v6, MMLU-Pro, MMLU-Redux), while Qwen3 VL 4B Instruct is better at 1 benchmark (WritingBench).
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 4.7x more expensive than Qwen3 VL 4B Instruct ($0.10/1M tokens).
For output processing, Kimi K2-Thinking-0905 ($2.00/1M tokens) is 3.3x more expensive than Qwen3 VL 4B Instruct ($0.60/1M tokens).
In conclusion, Kimi K2-Thinking-0905 is more expensive than Qwen3 VL 4B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2-Thinking-0905 has 996.0B more parameters than Qwen3 VL 4B Instruct, making it 24900.0% 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
Documented input modalities across available providers
Qwen3 VL 4B Instruct supports multimodal inputs, whereas Kimi K2-Thinking-0905 does not.
Qwen3 VL 4B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2-Thinking-0905
Qwen3 VL 4B Instruct
License
Usage and distribution terms
Kimi K2-Thinking-0905 is licensed under MIT, while Qwen3 VL 4B 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 4B Instruct was released on 2025-09-22.
Qwen3 VL 4B Instruct is 1 month newer than Kimi K2-Thinking-0905.
Sep 5, 2025
1.0 years ago
Sep 22, 2025
12 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 4B Instruct is available from DeepInfra.
Kimi K2-Thinking-0905
Qwen3 VL 4B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2-Thinking-0905 and Qwen3 VL 4B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2-Thinking-0905 vs Qwen3 VL 4B Instruct.