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 Gemini 3.1 Pro: benchmark scores, pricing & comparison.
Side-by-side GPT-5.6 Luna vs Gemini 3.1 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.
Verdict: GPT-5.6 Luna ranks higher (#35 vs #44); Gemini 3.1 Pro wins 5 of 9 shared benchmarks; GPT-5.6 Luna costs less ($1.00/$6.00 vs $2.00/$12.0 per 1M tokens).
Rank #35 vs #44AskClash overall scores 51.9 vs 40.6.
Pricing $1.00/$6.00 vs $2.00/$12.0Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs Google.
GPT-5.6 Luna vs Gemini 3.1 Pro 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 | Gemini 3.1 Pro |
|---|---|---|
| Overall Score | 51.9 | 40.6 |
| Leaderboard Rank | #35 | #44 |
| ACB | 56.1 | — |
| RWT | 9.5 | — |
| Coding Agent Index | 75.0 | — |
| HLE | 39.5 | 51.4 |
| GPQA | 92.3 | 94.3 |
| SWE-bench | — | 80.6 |
| SWE-Pro | 62.7 | 54.2 |
| SWE-Atlas | 81.0 | — |
| Terminal-Bench | 11.6 | — |
| DeepSWE | 67.2 | 11.7 |
| GDPval-AA | 1592.0 | 1317.0 |
| MCP Atlas | — | 69.2 |
| Finance Agent | 55.0 | 43.0 |
| CharXiv | — | 80.2 |
| MMMU-Pro | 78.4 | 83.9 |
| ARC-AGI 2 | 59.5 | 77.1 |
| Tau2 | — | 99.3 |
| MRCR | 41.3 | 84.9 |
| Input Price (per 1M tokens) | $1.00 | $2.00 |
| Output Price (per 1M tokens) | $6.00 | $12.0 |
| Context Window | 1M | 1M |
| Benchmarks Published | 15 | 10 |
GPT-5.6 Luna vs Gemini 3.1 Pro head-to-head charts
GPT-5.6 Luna leads 5 and Gemini 3.1 Pro leads 5 of 10 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 LunaGemini 3.1 Pro
Overall
HLE
GPQA
SWE-Pro
DeepSWE
GDPval-AA
Finance Agent
MMMU-Pro
ARC-AGI 2
MRCR
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M
Gemini 3.1 Pro
Input$2.00
Output$12.0
Workload$4.40
Context1M
Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.
More GPT-5.6 Luna and Gemini 3.1 Pro comparisons
Explore how GPT-5.6 Luna and Gemini 3.1 Pro stack up against other top-ranked LLMs.
Claude Opus 5.5 vs GPT-5.6 LunaClaude Opus 5.5 vs Gemini 3.1 ProClaude Fable 5.1 vs GPT-5.6 LunaClaude Fable 5.1 vs Gemini 3.1 ProClaude Opus 5 vs GPT-5.6 LunaClaude Opus 5 vs Gemini 3.1 ProClaude Sonnet 5.5 vs GPT-5.6 LunaClaude Sonnet 5.5 vs Gemini 3.1 ProClaude Fable 5 vs GPT-5.6 LunaClaude Fable 5 vs Gemini 3.1 Pro
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.