Kimi K2 0905 vs Qwen3.8 Max
Qwen3.8 Max leads the LLM Stats Score 51.9 to 21.6. Kimi K2 0905 is 2.3x cheaper per token.
Moonshot AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8 Max leads the overall LLM Stats Score 51.9 to 21.6, ranking #11 overall.
In the 1 individual benchmarks reported for both models, Qwen3.8 Max wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Kimi K2 0905 is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2 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 0905
- cost matters — it's about 2.3x cheaper per token
- you process long inputs — it offers a 262,144 token context window
Choose Qwen3.8 Max
- overall performance matters — it scores 51.9 and ranks #11 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Aug 2026
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
6 reported for Kimi K2 0905 · 42 for Qwen3.8 Max
Kimi K2 0905 outperforms in 0 benchmarks, while Qwen3.8 Max is better at 1 benchmark (GPQA).
Qwen3.8 Max 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 0905 ($0.60/1M tokens) is 2.8x cheaper than Qwen3.8 Max ($1.65/1M tokens).
For output processing, Kimi K2 0905 ($2.50/1M tokens) is 2.0x cheaper than Qwen3.8 Max ($4.95/1M tokens).
In conclusion, Qwen3.8 Max is more expensive than Kimi K2 0905.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.8 Max has 1400.0B more parameters than Kimi K2 0905, making it 140.0% larger.
Context Window
Maximum input and output token capacity
Kimi K2 0905 accepts 262,144 input tokens compared to Qwen3.8 Max's 256,000 tokens. Kimi K2 0905 can generate longer responses up to 262,144 tokens, while Qwen3.8 Max is limited to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.8 Max supports multimodal inputs, whereas Kimi K2 0905 does not.
Qwen3.8 Max can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2 0905
Qwen3.8 Max
License
Usage and distribution terms
Kimi K2 0905 is licensed under a proprietary license, while Qwen3.8 Max uses Qwen3.8-Max License.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Qwen3.8-Max License
Open weights
Release Timeline
When each model was launched
Kimi K2 0905 was released on 2025-09-05, while Qwen3.8 Max was released on 2026-08-02.
Qwen3.8 Max is 11 months newer than Kimi K2 0905.
Sep 5, 2025
1.0 years ago
Aug 2, 2026
1 months ago
11mo 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 0905 is available from Novita. Qwen3.8 Max is available from DeepInfra, Fireworks, Novita, Together.
Kimi K2 0905
Qwen3.8 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2 0905 and Qwen3.8 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2 0905 vs Qwen3.8 Max.