LLM Comparison

GPT-5.6 Terra vs SWE-1.7: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 Terra vs SWE-1.7 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.

Open live leaderboardGPT-5.6 Terra model pageSWE-1.7 model page
Rank #8 vs #46AskClash overall scores 76.3 vs 26.4.
Pricing $2.50/$15.0 vs $0/$0Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs Cognition.

GPT-5.6 Terra vs SWE-1.7 benchmark comparison

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

MetricGPT-5.6 TerraSWE-1.7
Overall Score76.326.4
Leaderboard Rank#8#46
RWT8.58.5
Coding Agent Index77.0—
GPQA92.9—
SWE-Pro63.4—
SWE-Atlas81.0—
Terminal-Bench87.481.5
DeepSWE69.6—
Finance Agent52.4—
MMMU-Pro80.7—
ARC-AGI 283.9—
Tau286.3—
MRCR89.6—
Input Price (per 1M tokens)$2.50$0
Output Price (per 1M tokens)$15.0$0
Context Window1M256K
Benchmarks Published131

GPT-5.6 Terra vs SWE-1.7 head-to-head charts

GPT-5.6 Terra leads 2 and SWE-1.7 leads 0 of 3 shared benchmarks. SWE-1.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 TerraSWE-1.7
Overall
76.3GPT-5.6 Terra
26.4SWE-1.7
RWT
8.5GPT-5.6 Terra
8.5SWE-1.7
Terminal-Bench
87.4GPT-5.6 Terra
81.5SWE-1.7
GPT-5.6 Terra
Input$2.50
Output$15.0
Workload$5.50
Context1M
SWE-1.7
Input$0
Output$0
Workload$0.00
Context256K

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

More GPT-5.6 Terra and SWE-1.7 comparisons

Explore how GPT-5.6 Terra and SWE-1.7 stack up against other top-ranked LLMs.

Claude Fable 5 vs GPT-5.6 TerraClaude Fable 5 vs SWE-1.7GPT-5.6 Sol vs GPT-5.6 TerraGPT-5.6 Sol vs SWE-1.7Kimi K3 vs GPT-5.6 TerraKimi K3 vs SWE-1.7Claude Opus 4.8 vs GPT-5.6 TerraClaude Opus 4.8 vs SWE-1.7Grok 4.5 vs GPT-5.6 TerraGrok 4.5 vs SWE-1.7

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.