LLM Comparison

DeepSeek V4.1 Flash vs GPT-5.6 Luna: benchmark scores, pricing & comparison.

Side-by-side DeepSeek V4.1 Flash 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 #22 vs #24AskClash overall scores 61.7 vs 59.2.
Pricing $0.15/$0.60 vs $1.00/$6.00Input and output token prices per 1M tokens when published.
API vs ProprietaryDeepSeek vs OpenAI.

DeepSeek V4.1 Flash 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.

MetricDeepSeek V4.1 FlashGPT-5.6 Luna
Overall Score61.759.2
Leaderboard Rank#22#24
ACB56.1
RWT9.5
Coding Agent Index75.0
HLE36.839.5
GPQA90.992.3
SWE-Pro62.7
SWE-Atlas81.0
Terminal-Bench90.684.7
DeepSWE74.267.2
GDPval-AA1592.0
Finance Agent55.0
MMMU-Pro78.4
ARC-AGI 259.5
MRCR41.3
Input Price (per 1M tokens)$0.15$1.00
Output Price (per 1M tokens)$0.60$6.00
Context Window1M1M
Benchmarks Published414

DeepSeek V4.1 Flash vs GPT-5.6 Luna head-to-head charts

DeepSeek V4.1 Flash leads 3 and GPT-5.6 Luna leads 2 of 5 shared benchmarks. DeepSeek V4.1 Flash is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

DeepSeek V4.1 FlashGPT-5.6 Luna
Overall
61.7DeepSeek V4.1 Flash
59.2GPT-5.6 Luna
HLE
36.8DeepSeek V4.1 Flash
39.5GPT-5.6 Luna
GPQA
90.9DeepSeek V4.1 Flash
92.3GPT-5.6 Luna
Terminal-Bench
90.6DeepSeek V4.1 Flash
84.7GPT-5.6 Luna
DeepSWE
74.2DeepSeek V4.1 Flash
67.2GPT-5.6 Luna
DeepSeek V4.1 Flash
Input$0.15
Output$0.60
Workload$0.27
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 DeepSeek V4.1 Flash and GPT-5.6 Luna comparisons

Explore how DeepSeek V4.1 Flash 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.