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
Qwen3.8 Max 0902 vs DeepSeek V4 Flash Vision Exp: benchmark scores, pricing & comparison.
Side-by-side Qwen3.8 Max 0902 vs DeepSeek V4 Flash Vision Exp 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 #24AskClash overall scores 70.0 vs 48.7.
Pricing $2.00/$6.00 vs $0.14/$0.28Input and output token prices per 1M tokens when published.
Proprietary vs APIAlibaba vs DeepSeek.
Qwen3.8 Max 0902 vs DeepSeek V4 Flash Vision Exp benchmark comparison
Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.
| Metric | Qwen3.8 Max 0902 | DeepSeek V4 Flash Vision Exp |
|---|---|---|
| Overall Score | 70.0 | 48.7 |
| Leaderboard Rank | #9 | #24 |
| ACB | 59.0 | 58.6 |
| HLE | 43.6 | 37.0 |
| GPQA | 92.6 | 91.0 |
| IFEval | 82.8 | — |
| SWE-bench | — | 79.0 |
| SWE-Pro | 67.7 | — |
| SWE-Atlas | 66.3 | — |
| Terminal-Bench | 86.6 | 83.9 |
| DeepSWE | 69.3 | 59.3 |
| OSWorld | 86.1 | — |
| MCP Atlas | — | 69.0 |
| Finance Agent | — | 49.5 |
| CharXiv | 88.4 | — |
| MMMU-Pro | 82.7 | — |
| MRCR | 92.9 | 78.7 |
| Input Price (per 1M tokens) | $2.00 | $0.14 |
| Output Price (per 1M tokens) | $6.00 | $0.28 |
| Context Window | 1M | 1M |
| Benchmarks Published | 12 | 10 |
Qwen3.8 Max 0902 vs DeepSeek V4 Flash Vision Exp head-to-head charts
Qwen3.8 Max 0902 leads 7 and DeepSeek V4 Flash Vision Exp leads 0 of 7 shared benchmarks. DeepSeek V4 Flash Vision Exp is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.
Qwen3.8 Max 0902DeepSeek V4 Flash Vision Exp
Overall
ACB
HLE
GPQA
Terminal-Bench
DeepSWE
MRCR
Qwen3.8 Max 0902
Input$2.00
Output$6.00
Workload$3.20
Context1M
DeepSeek V4 Flash Vision Exp
Input$0.14
Output$0.28
Workload$0.20
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.