LLM Comparison

GPT-5.6 Luna vs Qwen3.8 Flash Next: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 Luna vs Qwen3.8 Flash Next 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 #19AskClash overall scores 59.1 vs 57.6.
Pricing $1.00/$6.00 vs $0.16/$0.47Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightOpenAI vs Alibaba.

GPT-5.6 Luna vs Qwen3.8 Flash Next 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 Flash Next
Overall Score59.157.6
Leaderboard Rank#17#19
ACB56.166.5
RWT9.5
Coding Agent Index75.0
HLE35.9
GPQA92.391.7
IFEval81.3
SWE-Pro62.762.5
SWE-Atlas81.0
Terminal-Bench84.7
DeepSWE67.258.7
GDPval-AA1592.01743.0
Finance Agent55.0
CharXiv90.6
MMMU-Pro78.4
ARC-AGI 259.5
MRCR41.3
Input Price (per 1M tokens)$1.00$0.16
Output Price (per 1M tokens)$6.00$0.47
Context Window1M1M
Benchmarks Published139

GPT-5.6 Luna vs Qwen3.8 Flash Next head-to-head charts

GPT-5.6 Luna leads 4 and Qwen3.8 Flash Next leads 2 of 6 shared benchmarks. Qwen3.8 Flash Next is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 LunaQwen3.8 Flash Next
Overall
59.1GPT-5.6 Luna
57.6Qwen3.8 Flash Next
ACB
56.1GPT-5.6 Luna
66.5Qwen3.8 Flash Next
GPQA
92.3GPT-5.6 Luna
91.7Qwen3.8 Flash Next
SWE-Pro
62.7GPT-5.6 Luna
62.5Qwen3.8 Flash Next
DeepSWE
67.2GPT-5.6 Luna
58.7Qwen3.8 Flash Next
GDPval-AA
1592.0GPT-5.6 Luna
1743.0Qwen3.8 Flash Next
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M
Qwen3.8 Flash Next
Input$0.16
Output$0.47
Workload$0.25
Context1M

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 Flash Next comparisons

Explore how GPT-5.6 Luna and Qwen3.8 Flash Next 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.