LLM Comparison

Qwen3.8 Flash Next vs GPT-6 Luna: benchmark scores, pricing & comparison.

Side-by-side Qwen3.8 Flash Next vs GPT-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.

Verdict: Qwen3.8 Flash Next ranks higher (#26 vs #43) and wins 3 of 5 shared benchmarks.

Rank #26 vs #43AskClash overall scores 57.3 vs 50.3.
Pricing $0.16/$0.47 vs $0.10/$0.50Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryAlibaba vs OpenAI.

Qwen3.8 Flash Next vs GPT-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-6 Luna
Overall Score57.350.3
Leaderboard Rank#26#43
ACB66.550.2
HLE35.938.5
GPQA91.7—
IFEval81.3—
SWE-Pro62.5—
Terminal-Bench25.2—
DeepSWE58.766.6
GDPval-AA1743.01431.9
Finance Agent—49.9
CharXiv90.6—
MMMU-Pro79.879.7
ARC-AGI 2—59.3
Input Price (per 1M tokens)$0.16$0.10
Output Price (per 1M tokens)$0.47$0.50
Context Window1M1.05M
Benchmarks Published1210

Qwen3.8 Flash Next vs GPT-6 Luna head-to-head charts

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

Qwen3.8 Flash NextGPT-6 Luna
Overall
57.3Qwen3.8 Flash Next
50.3GPT-6 Luna
ACB
66.5Qwen3.8 Flash Next
50.2GPT-6 Luna
HLE
35.9Qwen3.8 Flash Next
38.5GPT-6 Luna
DeepSWE
58.7Qwen3.8 Flash Next
66.6GPT-6 Luna
GDPval-AA
1743.0Qwen3.8 Flash Next
1431.9GPT-6 Luna
MMMU-Pro
79.8Qwen3.8 Flash Next
79.7GPT-6 Luna
Qwen3.8 Flash Next
Input$0.16
Output$0.47
Workload$0.25
Context1M
GPT-6 Luna
Input$0.10
Output$0.50
Workload$0.20
Context1.05M

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

More Qwen3.8 Flash Next and GPT-6 Luna comparisons

Explore how Qwen3.8 Flash Next and GPT-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.