LLM Comparison

GPT-5.6 Luna vs Qwen3.8 27B: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 Luna vs Qwen3.8 27B 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 #14 vs #17AskClash overall scores 64.3 vs 57.0.
Pricing $1.00/$6.00 vs $0.45/$3.20Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightOpenAI vs Alibaba.

GPT-5.6 Luna vs Qwen3.8 27B benchmark comparison

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

MetricGPT-5.6 LunaQwen3.8 27B
Overall Score64.357.0
Leaderboard Rank#14#17
ACB56.156.4
RWT9.5
Coding Agent Index75.0
HLE30.8
GPQA92.389.2
IFEval79.5
SWE-Pro62.761.7
SWE-Atlas81.0
Terminal-Bench84.773.0
DeepSWE67.242.2
OSWorld84.3
Finance Agent55.0
CharXiv90.2
MMMU-Pro78.4
ARC-AGI 259.5
MRCR41.3
Input Price (per 1M tokens)$1.00$0.45
Output Price (per 1M tokens)$6.00$3.20
Context Window1M262K
Benchmarks Published139

GPT-5.6 Luna vs Qwen3.8 27B head-to-head charts

GPT-5.6 Luna leads 5 and Qwen3.8 27B leads 1 of 6 shared benchmarks. Qwen3.8 27B is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 LunaQwen3.8 27B
Overall
64.3GPT-5.6 Luna
57.0Qwen3.8 27B
ACB
56.1GPT-5.6 Luna
56.4Qwen3.8 27B
GPQA
92.3GPT-5.6 Luna
89.2Qwen3.8 27B
SWE-Pro
62.7GPT-5.6 Luna
61.7Qwen3.8 27B
Terminal-Bench
84.7GPT-5.6 Luna
73.0Qwen3.8 27B
DeepSWE
67.2GPT-5.6 Luna
42.2Qwen3.8 27B
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M
Qwen3.8 27B
Input$0.45
Output$3.20
Workload$1.09
Context262K

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

More GPT-5.6 Luna and Qwen3.8 27B comparisons

Explore how GPT-5.6 Luna and Qwen3.8 27B 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.