Kimi K2.5 vs Qwen3 VL 8B Thinking
Kimi K2.5 significantly outperforms across most benchmarks. Qwen3 VL 8B Thinking is 1.8x cheaper per token.
Moonshot AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Kimi K2.5 outperforms in 13 benchmarks (AIME 2025, CharXiv-R, GPQA, InfoVQAtest, LiveCodeBench v6, LVBench, MathVision, MathVista-Mini, MMLU-Pro, MMMU-Pro, OCRBench, Video-MME, VideoMMMU), while Qwen3 VL 8B Thinking is better at 0 benchmarks. Kimi K2.5 significantly outperforms across most benchmarks.
On price, Qwen3 VL 8B Thinking is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 8B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose Kimi K2.5
- you want the strongest raw capability — it leads on 13 of 13 shared benchmarks
- you want the most recent training data — it shipped Jan 2026
Choose Qwen3 VL 8B Thinking
- cost matters — it's about 1.8x cheaper per token
- you process long inputs — it offers a 262,144 token context window
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
Kimi K2.5 outperforms in 13 benchmarks (AIME 2025, CharXiv-R, GPQA, InfoVQAtest, LiveCodeBench v6, LVBench, MathVision, MathVista-Mini, MMLU-Pro, MMMU-Pro, OCRBench, Video-MME, VideoMMMU), while Qwen3 VL 8B Thinking is better at 0 benchmarks.
Kimi K2.5 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Kimi K2.5 ($0.60/1M tokens) is 3.3x more expensive than Qwen3 VL 8B Thinking ($0.18/1M tokens).
For output processing, Kimi K2.5 ($3.00/1M tokens) is 1.4x more expensive than Qwen3 VL 8B Thinking ($2.09/1M tokens).
In conclusion, Kimi K2.5 is more expensive than Qwen3 VL 8B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.5 has 991.0B more parameters than Qwen3 VL 8B Thinking, making it 11011.1% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 8B Thinking accepts 262,144 input tokens compared to Kimi K2.5's 262,100 tokens. Qwen3 VL 8B Thinking can generate longer responses up to 262,144 tokens, while Kimi K2.5 is limited to 262,100 tokens.
Input Capabilities
Supported data types and modalities
Both Kimi K2.5 and Qwen3 VL 8B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K2.5
Qwen3 VL 8B Thinking
License
Usage and distribution terms
Kimi K2.5 is licensed under MIT, while Qwen3 VL 8B Thinking 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 K2.5 was released on 2026-01-27, while Qwen3 VL 8B Thinking was released on 2025-09-22.
Kimi K2.5 is 4 months newer than Qwen3 VL 8B Thinking.
Jan 27, 2026
7 months ago
4mo newerSep 22, 2025
11 months 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 K2.5 is available from Fireworks, Moonshot AI. Qwen3 VL 8B Thinking is available from DeepInfra.
Kimi K2.5
Qwen3 VL 8B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2.5 and Qwen3 VL 8B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.5 vs Qwen3 VL 8B Thinking.