LLM Comparison

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

Side-by-side GPT-5.6 Luna vs DeepSeek V4 Pro 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 #13 vs #53AskClash overall scores 68.2 vs 20.0.
Pricing $1.00/$6.00 vs $1.74/$3.48Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightOpenAI vs DeepSeek.

GPT-5.6 Luna vs DeepSeek V4 Pro benchmark comparison

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

MetricGPT-5.6 LunaDeepSeek V4 Pro
Overall Score68.220.0
Leaderboard Rank#13#53
RWT9.57.0
Coding Agent Index75.0
HLE7.7
GPQA92.372.9
MATH-50064.5
SWE-bench73.6
SWE-Pro62.752.1
SWE-Atlas81.0
Terminal-Bench84.759.1
DeepSWE67.2
MCP Atlas69.4
Finance Agent55.044.1
MMMU-Pro78.4
ARC-AGI 259.5
MRCR41.344.7
Input Price (per 1M tokens)$1.00$1.74
Output Price (per 1M tokens)$6.00$3.48
Context Window1M1M
Benchmarks Published128

GPT-5.6 Luna vs DeepSeek V4 Pro head-to-head charts

GPT-5.6 Luna leads 6 and DeepSeek V4 Pro leads 1 of 7 shared benchmarks. GPT-5.6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 LunaDeepSeek V4 Pro
Overall
68.2GPT-5.6 Luna
20.0DeepSeek V4 Pro
RWT
9.5GPT-5.6 Luna
7.0DeepSeek V4 Pro
GPQA
92.3GPT-5.6 Luna
72.9DeepSeek V4 Pro
SWE-Pro
62.7GPT-5.6 Luna
52.1DeepSeek V4 Pro
Terminal-Bench
84.7GPT-5.6 Luna
59.1DeepSeek V4 Pro
Finance Agent
55.0GPT-5.6 Luna
44.1DeepSeek V4 Pro
MRCR
41.3GPT-5.6 Luna
44.7DeepSeek V4 Pro
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M
DeepSeek V4 Pro
Input$1.74
Output$3.48
Workload$2.44
Context1M

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

More GPT-5.6 Luna and DeepSeek V4 Pro comparisons

Explore how GPT-5.6 Luna and DeepSeek V4 Pro 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.