Evaluation Overview
DeepSeek V4 Flash (284B MoE, 13B activated) is currently the most cost-effective top-tier model. This article provides a comprehensive evaluation from six dimensions, comparing against GPT-5.6 Sol, Claude Opus 5, and DeepSeek V4 Pro. All tests are based on the latest API version from August 2026.
1. Coding Capability Evaluation
| Test Item | V4 Flash | GPT-5.6 Sol | Claude Opus 5 |
|---|---|---|---|
| LeetCode Hard (Accuracy) | 78% | 85% | 88% |
| Code Refactoring (Quality Score/100) | 82 | 88 | 91 |
| Bug Fixing (Accuracy) | 75% | 80% | 83% |
| API Endpoint Generation (Accuracy) | 90% | 88% | 86% |
| SQL Generation (Accuracy) | 85% | 91% | 89% |
Conclusion: V4 Flash's coding capability is about 90% of Claude Opus 5, but at only 1/50 of the cost. It is fully sufficient for daily coding; for complex architectures, choose Pro or Opus 5.
2. Reasoning Capability Evaluation
| Test Item | V4 Flash (Non-thinking) | V4 Flash (Thinking) | GPT-5.6 Sol |
|---|---|---|---|
| Math Competition (AIME 2025) | 65% | 88% | 91% |
| Logical Reasoning (LogiQA) | 78% | 86% | 89% |
| Scientific Reasoning (GPQA) | 62% | 78% | 85% |
Conclusion: Thinking mode is V4 Flash's killer feature—reasoning capability improves by about 20%, narrowing the gap with GPT-5.6 Sol to within 5%.
3. Multilingual Evaluation
| Language | V4 Flash | GPT-5.6 Sol | Claude Opus 5 |
|---|---|---|---|
| Chinese (C-Eval) | 92% | 87% | 85% |
| English (MMLU) | 86% | 92% | 91% |
| Japanese (JMMLU) | 78% | 80% | 83% |
| Code-Mixed Q&A | 88% | 90% | 89% |
Conclusion: V4 Flash has the strongest Chinese capability, slightly weaker English than GPT-5.6. It is the top choice for Chinese-language scenarios.
4. Latency Test
| Metric | V4 Flash | V4 Pro | GPT-5.6 |
|---|---|---|---|
| First Token Latency (Average) | 0.3s | 0.6s | 0.8s |
| Generation Speed (tokens/s) | 80 | 45 | 50 |
| Thinking Mode First Token | 1.5s | 2.5s | — |
Conclusion: V4 Flash has the lowest latency among all models, making it suitable for real-time interactive scenarios. After enabling thinking mode, latency increases but remains acceptable.
5. Cost Efficiency Comparison
Based on 10,000 API calls (average 2000 input tokens + 500 output tokens):
| Model | Daily Cost (Cache Hit) | Monthly Cost |
|---|---|---|
| V4 Flash | ¥10.2 | ¥306 |
| V4 Pro | ¥30.3 | ¥909 |
| GPT-5.6 Sol | $103 (≈¥740) | ≈¥22,200 |
| Claude Opus 5 | $82 (≈¥590) | ≈¥17,700 |
Conclusion: V4 Flash's total cost of ownership (TCO) is only 1.4% of GPT-5.6 and 1.7% of Claude Opus 5.
6. Selection Recommendations
| If you... | Recommendation |
|---|---|
| Have limited budget but need high quality | V4 Flash—best for daily use |
| Need the strongest coding/reasoning | V4 Pro or Claude Opus 5 |
| Primarily use Chinese scenarios | V4 Flash—strongest Chinese capability |
| Need high-concurrency real-time services | V4 Flash—lowest latency, highest concurrency |
| Not cost-sensitive | GPT-5.6 Sol—overall strongest |