LLM Comparison

SWE-2 vs Grok 4.5: benchmark scores, pricing & comparison.

Side-by-side SWE-2 vs Grok 4.5 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.

Rank #10 vs #21AskClash overall scores 66.3 vs 61.5.
Pricing $0/$0 vs $2.00/$6.00Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryCognition vs xAI.

SWE-2 vs Grok 4.5 benchmark comparison

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

MetricSWE-2Grok 4.5
Overall Score66.361.5
Leaderboard Rank#10#21
ACB60.057.6
RWT8.59.0
Coding Agent Index51.976.4
HLE43.540.3
GPQA93.593.1
SWE-Pro64.7
SWE-Atlas66.183.9
Terminal-Bench92.883.3
DeepSWE73.0
GDPval-AA1668.01542.8
MCP Atlas84.2
Finance Agent54.448.3
CharXiv84.8
MMMU-Pro81.680.4
ARC-AGI 252.6
Input Price (per 1M tokens)$0$2.00
Output Price (per 1M tokens)$0$6.00
Context Window256K500K
Benchmarks Published1312

SWE-2 vs Grok 4.5 head-to-head charts

SWE-2 leads 8 and Grok 4.5 leads 3 of 11 shared benchmarks. SWE-2 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

SWE-2Grok 4.5
Overall
66.3SWE-2
61.5Grok 4.5
ACB
60.0SWE-2
57.6Grok 4.5
RWT
8.5SWE-2
9.0Grok 4.5
Coding Agent Index
51.9SWE-2
76.4Grok 4.5
HLE
43.5SWE-2
40.3Grok 4.5
GPQA
93.5SWE-2
93.1Grok 4.5
SWE-Atlas
66.1SWE-2
83.9Grok 4.5
Terminal-Bench
92.8SWE-2
83.3Grok 4.5
GDPval-AA
1668.0SWE-2
1542.8Grok 4.5
Finance Agent
54.4SWE-2
48.3Grok 4.5
MMMU-Pro
81.6SWE-2
80.4Grok 4.5
SWE-2
Input$0
Output$0
Workload$0.00
Context256K
Grok 4.5
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 SWE-2 and Grok 4.5 comparisons

Explore how SWE-2 and Grok 4.5 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.