Benchmark scores
Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.
LLM Comparison
GPT-5.6 Luna vs DeepSeek V4 Pro 0813: benchmark scores, pricing & comparison.
Side-by-side GPT-5.6 Luna vs DeepSeek V4 Pro 0813 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 #15 vs #17AskClash overall scores 64.9 vs 60.5.
Pricing $1.00/$6.00 vs $0.43/$0.87Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightOpenAI vs DeepSeek.
GPT-5.6 Luna vs DeepSeek V4 Pro 0813 benchmark comparison
Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.
| Metric | GPT-5.6 Luna | DeepSeek V4 Pro 0813 |
|---|---|---|
| Overall Score | 64.9 | 60.5 |
| Leaderboard Rank | #15 | #17 |
| RWT | 9.5 | 7.0 |
| Coding Agent Index | 75.0 | — |
| HLE | — | 42.7 |
| GPQA | 92.3 | 90.1 |
| SWE-bench | — | 80.6 |
| SWE-Pro | 62.7 | 55.4 |
| SWE-Atlas | 81.0 | — |
| Terminal-Bench | 84.7 | 87.9 |
| DeepSWE | 67.2 | 62.7 |
| MCP Atlas | — | 73.6 |
| Finance Agent | 55.0 | — |
| MMMU-Pro | 78.4 | — |
| ARC-AGI 2 | 59.5 | — |
| MRCR | 41.3 | — |
| Input Price (per 1M tokens) | $1.00 | $0.43 |
| Output Price (per 1M tokens) | $6.00 | $0.87 |
| Context Window | 1M | 1M |
| Benchmarks Published | 13 | 9 |
GPT-5.6 Luna vs DeepSeek V4 Pro 0813 head-to-head charts
GPT-5.6 Luna leads 5 and DeepSeek V4 Pro 0813 leads 1 of 6 shared benchmarks. DeepSeek V4 Pro 0813 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.
GPT-5.6 LunaDeepSeek V4 Pro 0813
Overall
RWT
GPQA
SWE-Pro
Terminal-Bench
DeepSWE
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M
DeepSeek V4 Pro 0813
Input$0.43
Output$0.87
Workload$0.61
Context1M
Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.
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How to read this comparison
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.