LLM Comparison

GPT-6.1 Sol vs Mistral Large 4: benchmark scores, pricing & comparison.

Side-by-side GPT-6.1 Sol vs Mistral Large 4 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-6.1 Sol ranks higher (#6 vs #30) and wins 3 of 4 shared benchmarks; Mistral Large 4 costs less ($0.68/$2.09 vs $2.00/$10.0 per 1M tokens).

Rank #6 vs #30AskClash overall scores 72.5 vs 52.5.
Pricing $2.00/$10.0 vs $0.68/$2.09Input and output token prices per 1M tokens when published.
Proprietary vs PendingOpenAI vs Mistral.

GPT-6.1 Sol vs Mistral Large 4 benchmark comparison

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

MetricGPT-6.1 SolMistral Large 4
Overall Score72.552.5
Leaderboard Rank#6#30
ACB69.9—
Coding Agent Index62.9—
HLE52.935.0
GPQA95.0—
SWE-Atlas61.0—
Terminal-Bench56.1—
DeepSWE75.2—
GDPval-AA1575.11423.9
Finance Agent52.054.7
MMMU-Pro86.076.4
ARC-AGI 294.2—
Input Price (per 1M tokens)$2.00$0.68
Output Price (per 1M tokens)$10.0$2.09
Context Window1.05M1M
Benchmarks Published147

GPT-6.1 Sol vs Mistral Large 4 head-to-head charts

GPT-6.1 Sol leads 4 and Mistral Large 4 leads 1 of 5 shared benchmarks. Mistral Large 4 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-6.1 SolMistral Large 4
Overall
72.5GPT-6.1 Sol
52.5Mistral Large 4
HLE
52.9GPT-6.1 Sol
35.0Mistral Large 4
GDPval-AA
1575.1GPT-6.1 Sol
1423.9Mistral Large 4
Finance Agent
52.0GPT-6.1 Sol
54.7Mistral Large 4
MMMU-Pro
86.0GPT-6.1 Sol
76.4Mistral Large 4
GPT-6.1 Sol
Input$2.00
Output$10.0
Workload$4.00
Context1.05M
Mistral Large 4
Input$0.68
Output$2.09
Workload$1.10
Context1M

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

More GPT-6.1 Sol and Mistral Large 4 comparisons

Explore how GPT-6.1 Sol and Mistral Large 4 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.