LLM Comparison

Qwen3.8 27B vs Kimi K2.7: benchmark scores, pricing & comparison.

Side-by-side Qwen3.8 27B 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 #17 vs #23AskClash overall scores 57.0 vs 51.9.
Pricing $0.45/$3.20 vs $0.95/$4.00Input and output token prices per 1M tokens when published.
Open Weight vs Open WeightAlibaba vs Moonshot AI.

Qwen3.8 27B 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 27BKimi K2.7
Overall Score57.051.9
Leaderboard Rank#17#23
ACB56.443.0
RWT7.5
HLE30.854.0
GPQA89.290.5
IFEval79.5
SWE-bench80.2
SWE-Pro61.758.6
Terminal-Bench73.066.7
DeepSWE42.230.5
OSWorld84.373.1
MCP Atlas76.0
Finance Agent44.9
CharXiv90.280.4
MMMU-Pro79.4
Tau290.1
Input Price (per 1M tokens)$0.45$0.95
Output Price (per 1M tokens)$3.20$4.00
Context Window262K256K
Benchmarks Published914

Qwen3.8 27B vs Kimi K2.7 head-to-head charts

Qwen3.8 27B leads 7 and Kimi K2.7 leads 2 of 9 shared benchmarks. Qwen3.8 27B is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Qwen3.8 27BKimi K2.7
Overall
57.0Qwen3.8 27B
51.9Kimi K2.7
ACB
56.4Qwen3.8 27B
43.0Kimi K2.7
HLE
30.8Qwen3.8 27B
54.0Kimi K2.7
GPQA
89.2Qwen3.8 27B
90.5Kimi K2.7
SWE-Pro
61.7Qwen3.8 27B
58.6Kimi K2.7
Terminal-Bench
73.0Qwen3.8 27B
66.7Kimi K2.7
DeepSWE
42.2Qwen3.8 27B
30.5Kimi K2.7
OSWorld
84.3Qwen3.8 27B
73.1Kimi K2.7
CharXiv
90.2Qwen3.8 27B
80.4Kimi K2.7
Qwen3.8 27B
Input$0.45
Output$3.20
Workload$1.09
Context262K
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 27B and Kimi K2.7 comparisons

Explore how Qwen3.8 27B 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.