LLM Comparison

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

Side-by-side Qwen3.8 Flash Next vs GPT-5.6 Luna 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 #16 vs #18AskClash overall scores 57.9 vs 55.0.
Pricing $0.16/$0.47 vs $1.00/$6.00Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryAlibaba vs OpenAI.

Qwen3.8 Flash Next vs GPT-5.6 Luna benchmark comparison

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

MetricQwen3.8 Flash NextGPT-5.6 Luna
Overall Score57.955.0
Leaderboard Rank#16#18
ACB66.556.1
RWT9.5
Coding Agent Index75.0
HLE35.9
GPQA91.792.3
IFEval81.3
SWE-Pro62.562.7
SWE-Atlas81.0
Terminal-Bench84.7
DeepSWE58.767.2
GDPval-AA1743.01592.0
Finance Agent55.0
CharXiv90.6
MMMU-Pro78.4
ARC-AGI 259.5
MRCR41.3
Input Price (per 1M tokens)$0.16$1.00
Output Price (per 1M tokens)$0.47$6.00
Context Window1M1M
Benchmarks Published913

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

Qwen3.8 Flash Next leads 3 and GPT-5.6 Luna leads 3 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.

Qwen3.8 Flash NextGPT-5.6 Luna
Overall
57.9Qwen3.8 Flash Next
55.0GPT-5.6 Luna
ACB
66.5Qwen3.8 Flash Next
56.1GPT-5.6 Luna
GPQA
91.7Qwen3.8 Flash Next
92.3GPT-5.6 Luna
SWE-Pro
62.5Qwen3.8 Flash Next
62.7GPT-5.6 Luna
DeepSWE
58.7Qwen3.8 Flash Next
67.2GPT-5.6 Luna
GDPval-AA
1743.0Qwen3.8 Flash Next
1592.0GPT-5.6 Luna
Qwen3.8 Flash Next
Input$0.16
Output$0.47
Workload$0.25
Context1M
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M

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

More Qwen3.8 Flash Next and GPT-5.6 Luna comparisons

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