LLM Comparison

Claude Sonnet 5 vs DeepSeek V4 Pro: benchmark scores, pricing & comparison.

Side-by-side Claude Sonnet 5 vs DeepSeek V4 Pro 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 #9 vs #53AskClash overall scores 76.2 vs 20.0.
Pricing $3.00/$15.0 vs $1.74/$3.48Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightAnthropic vs DeepSeek.

Claude Sonnet 5 vs DeepSeek V4 Pro benchmark comparison

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

MetricClaude Sonnet 5DeepSeek V4 Pro
Overall Score76.220.0
Leaderboard Rank#9#53
RWT8.57.0
HLE57.47.7
GPQA91.172.9
MATH-50064.5
SWE-bench85.273.6
SWE-Pro63.252.1
Terminal-Bench80.459.1
DeepSWE53.8
OSWorld81.2
MCP Atlas69.4
Finance Agent44.1
CharXiv88.3
MMMU-Pro77.3
MRCR44.7
Input Price (per 1M tokens)$3.00$1.74
Output Price (per 1M tokens)$15.0$3.48
Context Window1M1M
Benchmarks Published118

Claude Sonnet 5 vs DeepSeek V4 Pro head-to-head charts

Claude Sonnet 5 leads 7 and DeepSeek V4 Pro leads 0 of 7 shared benchmarks. DeepSeek V4 Pro is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Claude Sonnet 5DeepSeek V4 Pro
Overall
76.2Claude Sonnet 5
20.0DeepSeek V4 Pro
RWT
8.5Claude Sonnet 5
7.0DeepSeek V4 Pro
HLE
57.4Claude Sonnet 5
7.7DeepSeek V4 Pro
GPQA
91.1Claude Sonnet 5
72.9DeepSeek V4 Pro
SWE-bench
85.2Claude Sonnet 5
73.6DeepSeek V4 Pro
SWE-Pro
63.2Claude Sonnet 5
52.1DeepSeek V4 Pro
Terminal-Bench
80.4Claude Sonnet 5
59.1DeepSeek V4 Pro
Claude Sonnet 5
Input$3.00
Output$15.0
Workload$6.00
Context1M
DeepSeek V4 Pro
Input$1.74
Output$3.48
Workload$2.44
Context1M

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

More Claude Sonnet 5 and DeepSeek V4 Pro comparisons

Explore how Claude Sonnet 5 and DeepSeek V4 Pro 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.