LLM Comparison

Claude Haiku 5.5 vs Kimi K2.7: benchmark scores, pricing & comparison.

Side-by-side Claude Haiku 5.5 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.

Verdict: Claude Haiku 5.5 ranks higher (#16 vs #42) and wins 3 of 3 shared benchmarks; Claude Haiku 5.5 costs less ($0.10/$0.50 vs $0.95/$4.00 per 1M tokens).

Rank #16 vs #42AskClash overall scores 60.0 vs 41.8.
Pricing $0.10/$0.50 vs $0.95/$4.00Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightAnthropic vs Moonshot AI.

Claude Haiku 5.5 vs Kimi K2.7 benchmark comparison

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

MetricClaude Haiku 5.5Kimi K2.7
Overall Score60.041.8
Leaderboard Rank#16#42
ACB63.543.0
RWT—7.5
HLE57.435.0
GPQA—89.6
IFEval—63.1
SWE-Pro64.8—
Terminal-Bench32.8—
DeepSWE—30.5
GDPval-AA1620.01040.1
MCP Atlas—76.0
Tau2—90.1
Input Price (per 1M tokens)$0.10$0.95
Output Price (per 1M tokens)$0.50$4.00
Context Window1M256K
Benchmarks Published89

Claude Haiku 5.5 vs Kimi K2.7 head-to-head charts

Claude Haiku 5.5 leads 4 and Kimi K2.7 leads 0 of 4 shared benchmarks. Claude Haiku 5.5 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Claude Haiku 5.5Kimi K2.7
Overall
60.0Claude Haiku 5.5
41.8Kimi K2.7
ACB
63.5Claude Haiku 5.5
43.0Kimi K2.7
HLE
57.4Claude Haiku 5.5
35.0Kimi K2.7
GDPval-AA
1620.0Claude Haiku 5.5
1040.1Kimi K2.7
Claude Haiku 5.5
Input$0.10
Output$0.50
Workload$0.20
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 Claude Haiku 5.5 and Kimi K2.7 comparisons

Explore how Claude Haiku 5.5 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.