LLM Comparison

Qwen3.8 Max vs Kimi K2.7: benchmark scores, pricing & comparison.

Side-by-side Qwen3.8 Max vs Kimi K2.7 comparison across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, context window, and AskClash RWT. Green marks the winner on each benchmark.

Rank #5 vs #52AskClash overall scores 70.9 vs 44.8.
Pricing $2.00/$6.00 vs $0.95/$4.00Input and output token prices per 1M tokens when published.
Open Weight vs Open WeightAlibaba vs Moonshot AI.

Qwen3.8 Max vs Kimi K2.7 benchmark comparison

Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.

MetricQwen3.8 MaxKimi K2.7
Overall Score70.944.8
Leaderboard Rank#5#52
ACB69.343.0
RWT7.5
Coding Agent Index43.3
HLE43.635.0
GPQA92.689.6
IFEval82.863.1
SWE-Pro67.7
SWE-Atlas66.3
Terminal-Bench86.644.7
DeepSWE69.330.5
GDPval-AA1689.01114.5
OSWorld86.1
MCP Atlas76.0
Finance Agent50.6
CharXiv88.4
MMMU-Pro82.7
Tau290.1
MRCR92.9
Input Price (per 1M tokens)$2.00$0.95
Output Price (per 1M tokens)$6.00$4.00
Context Window1M256K
Benchmarks Published1610

Qwen3.8 Max vs Kimi K2.7 head-to-head charts

Qwen3.8 Max leads 8 and Kimi K2.7 leads 0 of 8 shared benchmarks. Kimi K2.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Qwen3.8 MaxKimi K2.7
Overall
70.9Qwen3.8 Max
44.8Kimi K2.7
ACB
69.3Qwen3.8 Max
43.0Kimi K2.7
HLE
43.6Qwen3.8 Max
35.0Kimi K2.7
GPQA
92.6Qwen3.8 Max
89.6Kimi K2.7
IFEval
82.8Qwen3.8 Max
63.1Kimi K2.7
Terminal-Bench
86.6Qwen3.8 Max
44.7Kimi K2.7
DeepSWE
69.3Qwen3.8 Max
30.5Kimi K2.7
GDPval-AA
1689.0Qwen3.8 Max
1114.5Kimi K2.7
Qwen3.8 Max
Input$2.00
Output$6.00
Workload$3.20
Context1M
Kimi K2.7
Input$0.95
Output$4.00
Workload$1.75
Context256K

Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.

More Qwen3.8 Max and Kimi K2.7 comparisons

Explore how Qwen3.8 Max and Kimi K2.7 stack up against other top-ranked LLMs.

How to read this comparison

Benchmark scores

Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.

Token pricing

Lower is better for input and output prices. Green marks the cheaper model per 1M tokens.

Coverage matters

Models with fewer disclosed benchmark cells may have inflated percentile scores. Check the benchmark cell count for context.

This comparison page is generated from the AskClash LLM leaderboard cache. Open the live leaderboard for real-time scores and interactive filtering.