LLM Comparison

SWE-1.7 vs DeepSeek V4 Pro: benchmark scores, pricing & comparison.

Side-by-side SWE-1.7 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.

Open live leaderboardSWE-1.7 model pageDeepSeek V4 Pro model page
Rank #44 vs #50AskClash overall scores 26.6 vs 20.5.
Pricing $0/$0 vs $1.74/$3.48Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightCognition vs DeepSeek.

SWE-1.7 vs DeepSeek V4 Pro benchmark comparison

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

MetricSWE-1.7DeepSeek V4 Pro
Overall Score26.620.5
Leaderboard Rank#44#50
RWT8.57.0
HLE—7.7
GPQA—72.9
MATH-500—64.5
SWE-bench—73.6
SWE-Pro—52.1
Terminal-Bench81.559.1
MCP Atlas—69.4
Finance Agent—44.1
MRCR—44.7
Input Price (per 1M tokens)$0$1.74
Output Price (per 1M tokens)$0$3.48
Context Window256K1M
Benchmarks Published18

SWE-1.7 vs DeepSeek V4 Pro head-to-head charts

SWE-1.7 leads 3 and DeepSeek V4 Pro 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.

SWE-1.7DeepSeek V4 Pro
Overall
26.6SWE-1.7
20.5DeepSeek V4 Pro
RWT
8.5SWE-1.7
7.0DeepSeek V4 Pro
Terminal-Bench
81.5SWE-1.7
59.1DeepSeek V4 Pro
SWE-1.7
Input$0
Output$0
Workload$0.00
Context256K
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 SWE-1.7 and DeepSeek V4 Pro comparisons

Explore how SWE-1.7 and DeepSeek V4 Pro stack up against other top-ranked LLMs.

Claude Fable 5 vs SWE-1.7Claude Fable 5 vs DeepSeek V4 ProGPT-5.6 Sol vs SWE-1.7GPT-5.6 Sol vs DeepSeek V4 ProClaude Opus 4.8 vs SWE-1.7Claude Opus 4.8 vs DeepSeek V4 ProGrok 4.5 vs SWE-1.7Grok 4.5 vs DeepSeek V4 ProGPT-5.5 vs SWE-1.7GPT-5.5 vs DeepSeek V4 Pro

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.