LLM Comparison

Claude Sonnet 5.5 vs MiMo-V2.6-Pro: benchmark scores, pricing & comparison.

Side-by-side Claude Sonnet 5.5 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: Claude Sonnet 5.5 ranks higher (#2 vs #29) and wins 4 of 4 shared benchmarks; MiMo-V2.6-Pro costs less ($0.43/$0.87 vs $2.00/$10.0 per 1M tokens).

Rank #2 vs #29AskClash overall scores 74.9 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 WeightAnthropic vs Xiaomi.

Claude Sonnet 5.5 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.

MetricClaude Sonnet 5.5MiMo-V2.6-Pro
Overall Score74.951.9
Leaderboard Rank#2#29
ACB76.6—
HLE64.549.4
SWE-Pro81.3—
Terminal-Bench63.634.9
DeepSWE71.0—
GDPval-AA1837.81689.4
Finance Agent58.157.3
Input Price (per 1M tokens)$2.00$0.43
Output Price (per 1M tokens)$10.0$0.87
Context Window1M1M
Benchmarks Published107

Claude Sonnet 5.5 vs MiMo-V2.6-Pro head-to-head charts

Claude Sonnet 5.5 leads 5 and MiMo-V2.6-Pro leads 0 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.

Claude Sonnet 5.5MiMo-V2.6-Pro
Overall
74.9Claude Sonnet 5.5
51.9MiMo-V2.6-Pro
HLE
64.5Claude Sonnet 5.5
49.4MiMo-V2.6-Pro
Terminal-Bench
63.6Claude Sonnet 5.5
34.9MiMo-V2.6-Pro
GDPval-AA
1837.8Claude Sonnet 5.5
1689.4MiMo-V2.6-Pro
Finance Agent
58.1Claude Sonnet 5.5
57.3MiMo-V2.6-Pro
Claude Sonnet 5.5
Input$2.00
Output$10.0
Workload$4.00
Context1M
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 Claude Sonnet 5.5 and MiMo-V2.6-Pro comparisons

Explore how Claude Sonnet 5.5 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.