LLM Comparison

GPT-5.6 Terra vs Grok 4.6: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 Terra vs Grok 4.6 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-5.6 Terra ranks higher (#20 vs #24); Grok 4.6 costs less ($2.00/$6.00 vs $2.50/$15.0 per 1M tokens).

Rank #20 vs #24AskClash overall scores 56.3 vs 53.2.
Pricing $2.50/$15.0 vs $2.00/$6.00Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs xAI.

GPT-5.6 Terra vs Grok 4.6 benchmark comparison

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

MetricGPT-5.6 TerraGrok 4.6
Overall Score56.353.2
Leaderboard Rank#20#24
ACB56.469.0
RWT8.59.0
Coding Agent Index77.0—
HLE42.9—
GPQA92.9—
IFEval71.2—
SWE-Pro63.4—
SWE-Atlas81.0—
Terminal-Bench35.421.2
DeepSWE69.6—
GDPval-AA1593.0—
Finance Agent54.453.7
MMMU-Pro80.7—
ARC-AGI 283.9—
Tau286.3—
MRCR89.6—
Input Price (per 1M tokens)$2.50$2.00
Output Price (per 1M tokens)$15.0$6.00
Context Window1M500K
Benchmarks Published154

GPT-5.6 Terra vs Grok 4.6 head-to-head charts

GPT-5.6 Terra leads 3 and Grok 4.6 leads 2 of 5 shared benchmarks. Grok 4.6 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 TerraGrok 4.6
Overall
56.3GPT-5.6 Terra
53.2Grok 4.6
ACB
56.4GPT-5.6 Terra
69.0Grok 4.6
RWT
8.5GPT-5.6 Terra
9.0Grok 4.6
Terminal-Bench
35.4GPT-5.6 Terra
21.2Grok 4.6
Finance Agent
54.4GPT-5.6 Terra
53.7Grok 4.6
GPT-5.6 Terra
Input$2.50
Output$15.0
Workload$5.50
Context1M
Grok 4.6
Input$2.00
Output$6.00
Workload$3.20
Context500K

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

More GPT-5.6 Terra and Grok 4.6 comparisons

Explore how GPT-5.6 Terra and Grok 4.6 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.