Benchmark scores
Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.
LLM Comparison
DeepSeek V4 Pro vs SWE-1.7: benchmark scores, pricing & comparison.
Side-by-side DeepSeek V4 Pro 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.
Rank #23 vs #44AskClash overall scores 54.7 vs 28.8.
Pricing $1.74/$3.48 vs $0/$0Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryDeepSeek vs Cognition.
DeepSeek V4 Pro vs SWE-1.7 benchmark comparison
Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.
| Metric | DeepSeek V4 Pro | SWE-1.7 |
|---|---|---|
| Overall Score | 54.7 | 28.8 |
| Leaderboard Rank | #23 | #44 |
| RWT | 7.0 | 9.0 |
| HLE | 37.7 | — |
| GPQA | 90.1 | — |
| MATH-500 | 64.5 | — |
| SWE-bench | 80.6 | — |
| SWE-Pro | 55.4 | — |
| Terminal-Bench | 67.9 | 81.5 |
| LiveCodeBench | 93.5 | — |
| MCP Atlas | 73.6 | — |
| Tau2 | 96.2 | — |
| MRCR | 83.5 | — |
| Input Price (per 1M tokens) | $1.74 | $0 |
| Output Price (per 1M tokens) | $3.48 | $0 |
| Context Window | 1M | 256K |
| Benchmark Cells | 10 | 1 |
More DeepSeek V4 Pro and SWE-1.7 comparisons
Explore how DeepSeek V4 Pro and SWE-1.7 stack up against other top-ranked LLMs.
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How to read this comparison
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.