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 0813 vs Qwen3.8 Max 0902: benchmark scores, pricing & comparison.
Side-by-side DeepSeek V4 Pro 0813 vs Qwen3.8 Max 0902 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 #22 vs #27AskClash overall scores 53.1 vs 39.0.
Pricing $0.43/$0.87 vs $2.00/$6.00Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryDeepSeek vs Alibaba.
DeepSeek V4 Pro 0813 vs Qwen3.8 Max 0902 benchmark comparison
Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.
| Metric | DeepSeek V4 Pro 0813 | Qwen3.8 Max 0902 |
|---|---|---|
| Overall Score | 53.1 | 39.0 |
| Leaderboard Rank | #22 | #27 |
| ACB | 54.8 | — |
| RWT | 7.0 | — |
| HLE | 42.7 | 43.6 |
| GPQA | 90.1 | 92.6 |
| IFEval | — | 82.8 |
| SWE-bench | 80.6 | — |
| SWE-Pro | 55.4 | 67.7 |
| SWE-Atlas | — | 66.3 |
| Terminal-Bench | 87.9 | 86.6 |
| DeepSWE | 62.7 | 69.3 |
| GDPval-AA | 1554.0 | — |
| OSWorld | — | 86.1 |
| MCP Atlas | 73.6 | — |
| Finance Agent | 44.1 | — |
| CharXiv | — | 88.4 |
| MMMU-Pro | — | 82.7 |
| Tau2 | 96.2 | — |
| MRCR | — | 92.9 |
| Input Price (per 1M tokens) | $0.43 | $2.00 |
| Output Price (per 1M tokens) | $0.87 | $6.00 |
| Context Window | 1M | 1M |
| Benchmarks Published | 11 | 11 |
DeepSeek V4 Pro 0813 vs Qwen3.8 Max 0902 head-to-head charts
DeepSeek V4 Pro 0813 leads 2 and Qwen3.8 Max 0902 leads 4 of 6 shared benchmarks. DeepSeek V4 Pro 0813 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.
DeepSeek V4 Pro 0813Qwen3.8 Max 0902
Overall
HLE
GPQA
SWE-Pro
Terminal-Bench
DeepSWE
DeepSeek V4 Pro 0813
Input$0.43
Output$0.87
Workload$0.61
Context1M
Qwen3.8 Max 0902
Input$2.00
Output$6.00
Workload$3.20
Context1M
Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.
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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.