LLM Comparison

GPT-5.6 Luna vs GPT-6.1 Sol: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 Luna vs GPT-6.1 Sol 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 (#24 vs #30); GPT-6.1 Sol wins 2 of 2 shared benchmarks; GPT-5.6 Luna costs less ($1.00/$6.00 vs $2.00/$10.0 per 1M tokens).

Rank #24 vs #30AskClash overall scores 60.5 vs 58.1.
Pricing $1.00/$6.00 vs $2.00/$10.0Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs OpenAI.

GPT-5.6 Luna vs GPT-6.1 Sol benchmark comparison

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

MetricGPT-5.6 LunaGPT-6.1 Sol
Overall Score60.558.1
Leaderboard Rank#24#30
ACB56.161.9
RWT9.5—
Coding Agent Index75.0—
HLE39.5—
GPQA92.3—
SWE-Pro62.7—
SWE-Atlas81.0—
Terminal-Bench84.7—
DeepSWE67.275.2
GDPval-AA1592.0—
Finance Agent55.0—
MMMU-Pro78.4—
ARC-AGI 259.5—
MRCR41.3—
Input Price (per 1M tokens)$1.00$2.00
Output Price (per 1M tokens)$6.00$10.0
Context Window1M1.05M
Benchmarks Published153

GPT-5.6 Luna vs GPT-6.1 Sol head-to-head charts

GPT-5.6 Luna leads 1 and GPT-6.1 Sol leads 2 of 3 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 LunaGPT-6.1 Sol
Overall
60.5GPT-5.6 Luna
58.1GPT-6.1 Sol
ACB
56.1GPT-5.6 Luna
61.9GPT-6.1 Sol
DeepSWE
67.2GPT-5.6 Luna
75.2GPT-6.1 Sol
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M
GPT-6.1 Sol
Input$2.00
Output$10.0
Workload$4.00
Context1.05M

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

More GPT-5.6 Luna and GPT-6.1 Sol comparisons

Explore how GPT-5.6 Luna and GPT-6.1 Sol 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.