LLM Comparison

GPT-6.1 Sol vs Claude Haiku 5.5: benchmark scores, pricing & comparison.

Side-by-side GPT-6.1 Sol vs Claude Haiku 5.5 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 #16); Claude Haiku 5.5 costs less ($0.10/$0.50 vs $2.00/$10.0 per 1M tokens).

Rank #6 vs #16AskClash overall scores 72.1 vs 60.0.
Pricing $2.00/$10.0 vs $0.10/$0.50Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs Anthropic.

GPT-6.1 Sol vs Claude Haiku 5.5 benchmark comparison

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

MetricGPT-6.1 SolClaude Haiku 5.5
Overall Score72.160.0
Leaderboard Rank#6#16
ACB69.963.5
Coding Agent Index62.9—
HLE52.957.4
GPQA95.0—
SWE-Pro—64.8
SWE-Atlas61.0—
Terminal-Bench56.132.8
DeepSWE75.2—
GDPval-AA1575.11620.0
Finance Agent52.0—
MMMU-Pro86.0—
ARC-AGI 294.2—
Input Price (per 1M tokens)$2.00$0.10
Output Price (per 1M tokens)$10.0$0.50
Context Window1.05M1M
Benchmarks Published148

GPT-6.1 Sol vs Claude Haiku 5.5 head-to-head charts

GPT-6.1 Sol leads 3 and Claude Haiku 5.5 leads 2 of 5 shared benchmarks. Claude Haiku 5.5 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-6.1 SolClaude Haiku 5.5
Overall
72.1GPT-6.1 Sol
60.0Claude Haiku 5.5
ACB
69.9GPT-6.1 Sol
63.5Claude Haiku 5.5
HLE
52.9GPT-6.1 Sol
57.4Claude Haiku 5.5
Terminal-Bench
56.1GPT-6.1 Sol
32.8Claude Haiku 5.5
GDPval-AA
1575.1GPT-6.1 Sol
1620.0Claude Haiku 5.5
GPT-6.1 Sol
Input$2.00
Output$10.0
Workload$4.00
Context1.05M
Claude Haiku 5.5
Input$0.10
Output$0.50
Workload$0.20
Context1M

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

More GPT-6.1 Sol and Claude Haiku 5.5 comparisons

Explore how GPT-6.1 Sol and Claude Haiku 5.5 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.