V4-Pro-Max performance on 5 mainstream coding benchmarks + real test cases + scenario recommendations.
5 mainstream benchmarks + real test cases + V4 vs competitors.
| Benchmark | What it tests | V4-Pro-Max | Comparison |
|---|---|---|---|
| LiveCodeBench | Real-time competitive coding (anti-leak) | 93.5% | Open-source SOTA, above GPT-5.5 (~90%) |
| Codeforces Rating | Algorithmic contest rating (vs human players) | 3206 | Above Gemini-3.1-Pro and Claude Opus-4.6 |
| SWE-bench Verified | Real GitHub Issue fixes | 80.6% | Close to Claude Opus 4.6 (80.8%), ahead of GPT-5.5 (~80%) |
| HumanEval pass@1 | Python function generation (one-shot) | 90.8% | Above Claude 3.5 Sonnet, ties GPT-4o |
| AIME 2026 | American Invitational Math Exam | 99.4% | Near-perfect, ties GPT-5 series |
| IMO-AnswerBench | Math Olympiad | 89.8% | Open-source leading |
| HMMT 2026 | Harvard-MIT Math Tournament | 95.2% | Open-source leading |
| MMLU-Pro | Multi-subject reasoning | 87.5% | Open-source leading, close to GPT-5.5 (~89%) |
Source: DeepSeek V4 official technical report, April 2026.
Real cases from public tests to see V4's behavior in specific scenarios.
Task: Write an interactive HTML page with neon light effects, particle glitch animations, hover effects. V4-Pro thought for only 7 seconds and outputted working code with full animations and responsive layout.
✓ Pass on first tryTask: Implement 3D paper receipt with physics engine, mouse drag + tilt perspective + flip animation. V4's first generation came back blank, requiring 2-3 rounds of correction. Complex frontend aesthetic details are weaker than GPT-5.5 and Claude Opus.
△ Partial (2-3 rounds)Task: Localize and fix classic multithreaded counter counter += 1 non-atomic bug. V4 provided threading.Lock solution and analyzed why GIL doesn't protect this operation. GPT-4o also used Lock but emphasized data hazard explanation. Claude gave three-option comparison (Lock, atomic operations, concurrent.futures). V4's engineer-thinking was most direct, with Python/Java/Go fix examples.
✓ Pass on first try (strong)Task: Implement globally unique, monotonically increasing, high-performance distributed ID generator that handles clock rollback. GPT-4o gave a standard snowflake variant with exception on clock rollback. Claude's solution was more complete (backup segments + Redis segment caching) with architecture diagram. V4's output was the most production-ready — direct Spring Boot example with database segments + local caching, large code volume, suited for direct use by Java tech teams.
✓ Pass on first try (strong: production-ready)Task: Identify SQL injection risk in concatenated SQL code. Claude's first response included parameterized query improvements, emphasizing "never trust user input". GPT-4o also identified the issue but its response was more educational. V4 was most direct: gave Python/Java/Go injection-prevention examples, full engineer-thinking.
✓ Pass on first try (strong: three-language)Task: Optimize inefficient data processing code. V4 used generators, in-place operations, and vectorized thinking, reducing memory usage by 60%. GPT-4o's optimization direction was correct but changes were conservative. Claude gave a pandas vs native Python performance comparison table. V4 is the most decisive on aggressive refactoring.
✓ Pass on first try (strong: perf)Public data doesn't only tell the good story. A third-party 38-task test reveals V4's real weak spot.
V4-Pro (Thinking mode) averaged 8.90, slightly above Claude Opus 4.7's 8.87. But completion rate: V4 completed 29/38 (76%), Claude completed 38/38 (100%). The remaining 9 V4 tasks timed out — exactly the hardest coding and reasoning tasks. Conclusion: V4 ties top models on "medium difficulty" tasks, but Claude is more stable on the hardest tail tasks.
Animation details, visual hierarchy, style consistency weaker than Claude Opus and GPT-5.5. Recommend using V4 for logic + Claude/GPT for visuals.
V4 timed out on 9 of 38 tasks. Tasks involving multiple files, cross-module dependencies, maintaining backward compatibility — Claude Opus is more reliable.
AIME 2026 at 99.4% looks strong, but for true competition-level pure math reasoning (IMO/IOI), even with Think Max mode it's less stable than GPT-5.5.
V4's 1M context can ingest an entire code repo at once. Repo-level Q&A, cross-file dependency mapping, batch refactor suggestions are V4's strengths.
Java Spring Boot, Python backend, Go microservices — V4's output is the most production-ready, full engineer-thinking (see distributed ID case).
DeepSeek's official DeepCode tool is optimized for V4. V4 + reasoning_effort=high is the best price-performance for Agent workflows.
Daily coding: model=deepseek-v4-flash + reasoning_effort=high (fast + sufficient reasoning, $0.28/MTok output); Agent / complex tasks: model=deepseek-v4-pro + reasoning_effort=max (deep reasoning, $0.87/MTok output). Thinking mode is on by default, no extra config needed.
V4's 1M context can ingest an entire codebase, but multi-turn conversations beyond 15 rounds show context forgetting — put important decisions and confirmed interface signatures at the head of the messages list to reduce tail information being forgotten.
Production best practice: use V4 for daily coding (save money), route the hardest 5%-10% tasks to Claude Opus (ensure stability). An Agent router automatically dispatches by task complexity — Claude subscription + V4 API combo is 70% cheaper than pure Claude.
LiveCodeBench is a real-time coding evaluation started in 2024 (monthly updated problems to prevent data contamination). 93.5% means V4-Pro-Max can solve 93.5% of new LeetCode-difficulty problems — first among open-source models, and exceeding GPT-5.5 (~90%).
Codeforces Rating 3000+ is "International Master" tier, achieved by only ~500 people globally. 3206 means V4-Pro-Max exceeds most top human competitive programmers.
SWE-bench Verified is the closest benchmark to real engineering — testing the model's ability to solve real GitHub Issues. 80.6% means V4 can independently complete over 80% of real engineering tasks — practically tied with Claude Opus 4.6's 80.8%, at the industry ceiling.
The 9 V4-timed-out tasks share features: spanning 5+ files, needing backward compatibility, involving complex module dependencies. These tasks require sustained global attention; V4 still has room to improve on long-range reasoning stability.