Kimi K3 vs Qwen3.8-27B
Kimi K3 leads the LLM Stats Score 53.0 to 45.1. Qwen3.8-27B is 5.4x cheaper per token.
Moonshot AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Kimi K3 leads the overall LLM Stats Score 53.0 to 45.1, ranking #8 overall.
In the 8 individual benchmarks reported for both models, Kimi K3 wins 8; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8-27B is roughly 5.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K3 also accepts a larger context window (1,048,576 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 K3
- overall performance matters — it scores 53.0 and ranks #8 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 8 of 8 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
Choose Qwen3.8-27B
- cost matters — it's about 5.4x cheaper per token
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
31 reported for Kimi K3 · 26 for Qwen3.8-27B
Kimi K3 outperforms in 8 benchmarks (BabyVision, CharXiv-R, DeepSWE 1.1, GPQA, Humanity's Last Exam, Job Bench, MathVision, Terminal-Bench 2.1), while Qwen3.8-27B is better at 0 benchmarks.
Kimi K3 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 K3 ($2.85/1M tokens) is 7.1x more expensive than Qwen3.8-27B ($0.40/1M tokens).
For output processing, Kimi K3 ($14.25/1M tokens) is 4.8x more expensive than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, Kimi K3 is more expensive than Qwen3.8-27B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K3 has 2772.2B more parameters than Qwen3.8-27B, making it 9978.7% larger.
Context Window
Maximum input and output token capacity
Kimi K3 accepts 1,048,576 input tokens compared to Qwen3.8-27B's 262,144 tokens. Kimi K3 can generate longer responses up to 1,048,576 tokens, while Qwen3.8-27B is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
Both Kimi K3 and Qwen3.8-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K3
Qwen3.8-27B
License
Usage and distribution terms
Kimi K3 is licensed under Kimi K3 License, while Qwen3.8-27B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Kimi K3 License
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Kimi K3 was released on 2026-07-16, while Qwen3.8-27B was released on 2026-08-14.
Qwen3.8-27B is 1 month newer than Kimi K3.
Jul 16, 2026
2 months ago
Aug 14, 2026
1 months ago
4w 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 K3 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together. Qwen3.8-27B is available from DeepInfra, FriendliAI.
Kimi K3
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K3 and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K3 vs Qwen3.8-27B.