Kimi K2.7 Code vs Qwen3.8 Max
Qwen3.8 Max leads the LLM Stats Score 53.0 to 39.6. Kimi K2.7 Code is 1.7x cheaper per token.
Moonshot AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8 Max leads the overall LLM Stats Score 53.0 to 39.6, ranking #8 overall.
In the 2 individual benchmarks reported for both models, Qwen3.8 Max wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Kimi K2.7 Code is roughly 1.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2.7 Code 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 K2.7 Code
- cost matters — it's about 1.7x cheaper per token
- you process long inputs — it offers a 262,144 token context window
Choose Qwen3.8 Max
- 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 2 of 2 exact shared results
- 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
9 reported for Kimi K2.7 Code · 42 for Qwen3.8 Max
Kimi K2.7 Code outperforms in 0 benchmarks, while Qwen3.8 Max is better at 2 benchmarks (DeepSWE 1.1, MLS-Bench Lite).
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 K2.7 Code ($0.74/1M tokens) is 2.2x cheaper than Qwen3.8 Max ($1.65/1M tokens).
For output processing, Kimi K2.7 Code ($3.50/1M tokens) is 1.4x cheaper than Qwen3.8 Max ($4.95/1M tokens).
In conclusion, Qwen3.8 Max is more expensive than Kimi K2.7 Code.*
* 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 K2.7 Code, making it 140.0% larger.
Context Window
Maximum input and output token capacity
Kimi K2.7 Code accepts 262,144 input tokens compared to Qwen3.8 Max's 256,000 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both Kimi K2.7 Code and Qwen3.8 Max support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K2.7 Code
Qwen3.8 Max
License
Usage and distribution terms
Kimi K2.7 Code is licensed under Modified MIT 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.
Modified MIT License
Open weights
Qwen3.8-Max License
Open weights
Release Timeline
When each model was launched
Kimi K2.7 Code was released on 2026-06-12, while Qwen3.8 Max was released on 2026-08-02.
Qwen3.8 Max is 2 months newer than Kimi K2.7 Code.
Jun 12, 2026
2 months ago
Aug 2, 2026
3 weeks ago
1mo 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 K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together. Qwen3.8 Max is available from DeepInfra, Fireworks, Novita, Together.
Kimi K2.7 Code
Qwen3.8 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2.7 Code and Qwen3.8 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.7 Code vs Qwen3.8 Max.