LLM Comparison

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

Side-by-side GPT-6.1 Terra 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 Terra ranks higher (#35 vs #47); Claude Haiku 5.5 wins 3 of 4 shared benchmarks; Claude Haiku 5.5 costs less ($0.10/$0.50 vs $1.00/$5.00 per 1M tokens).

Rank #35 vs #47AskClash overall scores 61.7 vs 59.3.
Pricing $1.00/$5.00 vs $0.10/$0.50Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs Anthropic.

GPT-6.1 Terra 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 TerraClaude Haiku 5.5
Overall Score61.759.3
Leaderboard Rank#35#47
ACB62.163.5
HLE46.257.4
SWE-Pro—64.8
Terminal-Bench55.632.8
GDPval-AA1467.61620.0
Finance Agent—54.1
MMMU-Pro84.7—
Input Price (per 1M tokens)$1.00$0.10
Output Price (per 1M tokens)$5.00$0.50
Context Window1.05M1M
Benchmarks Published89

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

GPT-6.1 Terra leads 2 and Claude Haiku 5.5 leads 3 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 TerraClaude Haiku 5.5
Overall
61.7GPT-6.1 Terra
59.3Claude Haiku 5.5
ACB
62.1GPT-6.1 Terra
63.5Claude Haiku 5.5
HLE
46.2GPT-6.1 Terra
57.4Claude Haiku 5.5
Terminal-Bench
55.6GPT-6.1 Terra
32.8Claude Haiku 5.5
GDPval-AA
1467.6GPT-6.1 Terra
1620.0Claude Haiku 5.5
GPT-6.1 Terra
Input$1.00
Output$5.00
Workload$2.00
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 Terra and Claude Haiku 5.5 comparisons

Explore how GPT-6.1 Terra 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.