LLM Comparison

GPT-6.1 Sol vs MiMo-V2.6-Pro: benchmark scores, pricing & comparison.

Side-by-side GPT-6.1 Sol vs MiMo-V2.6-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-6.1 Sol ranks higher (#8 vs #29); MiMo-V2.6-Pro costs less ($0.43/$0.87 vs $2.00/$10.0 per 1M tokens).

Rank #8 vs #29AskClash overall scores 67.7 vs 51.9.
Pricing $2.00/$10.0 vs $0.43/$0.87Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightOpenAI vs Xiaomi.

GPT-6.1 Sol vs MiMo-V2.6-Pro benchmark comparison

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

MetricGPT-6.1 SolMiMo-V2.6-Pro
Overall Score67.751.9
Leaderboard Rank#8#29
ACB69.9—
Coding Agent Index62.9—
HLE52.949.4
GPQA95.0—
SWE-Atlas61.0—
Terminal-Bench56.134.9
DeepSWE75.2—
GDPval-AA1575.11689.4
Finance Agent52.057.3
MMMU-Pro86.0—
ARC-AGI 294.2—
Input Price (per 1M tokens)$2.00$0.43
Output Price (per 1M tokens)$10.0$0.87
Context Window1.05M1M
Benchmarks Published147

GPT-6.1 Sol vs MiMo-V2.6-Pro head-to-head charts

GPT-6.1 Sol leads 3 and MiMo-V2.6-Pro leads 2 of 5 shared benchmarks. MiMo-V2.6-Pro is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-6.1 SolMiMo-V2.6-Pro
Overall
67.7GPT-6.1 Sol
51.9MiMo-V2.6-Pro
HLE
52.9GPT-6.1 Sol
49.4MiMo-V2.6-Pro
Terminal-Bench
56.1GPT-6.1 Sol
34.9MiMo-V2.6-Pro
GDPval-AA
1575.1GPT-6.1 Sol
1689.4MiMo-V2.6-Pro
Finance Agent
52.0GPT-6.1 Sol
57.3MiMo-V2.6-Pro
GPT-6.1 Sol
Input$2.00
Output$10.0
Workload$4.00
Context1.05M
MiMo-V2.6-Pro
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.

More GPT-6.1 Sol and MiMo-V2.6-Pro comparisons

Explore how GPT-6.1 Sol and MiMo-V2.6-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.