LLM Comparison

GPT-5.6 Luna vs Hy4 Preview: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 Luna vs Hy4 Preview 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 #18AskClash overall scores 60.5 vs 58.2.
Pricing $1.00/$6.00 vs $0.83/$2.50Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightOpenAI vs Tencent.

GPT-5.6 Luna vs Hy4 Preview benchmark comparison

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

MetricGPT-5.6 LunaHy4 Preview
Overall Score60.558.2
Leaderboard Rank#17#18
ACB56.1
RWT9.58.5
Coding Agent Index75.0
HLE43.4
GPQA92.392.3
SWE-Pro62.765.7
SWE-Atlas81.0
Terminal-Bench84.785.4
DeepSWE67.264.3
GDPval-AA1592.01678.0
MCP Atlas83.7
Finance Agent55.0
MMMU-Pro78.4
ARC-AGI 259.5
MRCR41.3
Input Price (per 1M tokens)$1.00$0.83
Output Price (per 1M tokens)$6.00$2.50
Context Window1M1M
Benchmarks Published137

GPT-5.6 Luna vs Hy4 Preview head-to-head charts

GPT-5.6 Luna leads 3 and Hy4 Preview leads 3 of 7 shared benchmarks. Hy4 Preview is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 LunaHy4 Preview
Overall
60.5GPT-5.6 Luna
58.2Hy4 Preview
RWT
9.5GPT-5.6 Luna
8.5Hy4 Preview
GPQA
92.3GPT-5.6 Luna
92.3Hy4 Preview
SWE-Pro
62.7GPT-5.6 Luna
65.7Hy4 Preview
Terminal-Bench
84.7GPT-5.6 Luna
85.4Hy4 Preview
DeepSWE
67.2GPT-5.6 Luna
64.3Hy4 Preview
GDPval-AA
1592.0GPT-5.6 Luna
1678.0Hy4 Preview
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M
Hy4 Preview
Input$0.83
Output$2.50
Workload$1.33
Context1M

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

More GPT-5.6 Luna and Hy4 Preview comparisons

Explore how GPT-5.6 Luna and Hy4 Preview 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.