DeepSeek V4 vs Opus 4.6 vs GPT-5.4: Benchmark Comparison for Developers

Apr 25, 2026

When choosing an LLM for production, I’ve always faced the same trade-off: closed-source models like Claude Opus and GPT-5 deliver top performance but come with high API costs and data privacy concerns. Open-source models promised cost savings and self-hosting, but historically lagged in capability. DeepSeek V4 claims to close this gap. I decided to dig into the benchmarks to see if it’s true.

DeepSeek V4 Text Arena Ranking

What I Found

DeepSeek V4 Pro genuinely competes with Opus 4.6 Max and GPT-5.4 xHigh on knowledge and reasoning benchmarks—but there’s a catch. It still trails on complex agentic tasks. Here’s what the numbers actually mean for developers making deployment decisions.

Benchmark Breakdown

Knowledge & Reasoning: Competitive

I looked at the core academic benchmarks first because they’re the most objective measure:

| Benchmark     | DeepSeek V4 Pro | Opus 4.6 Max | GPT-5.4 xHigh | Gemini 3.1 Pro |
|--------------|-----------------|--------------|---------------|----------------|
| MMLU         | 90.1%           | ~92%         | ~91%          | ~91.5%         |
| MMLU-Pro     | 73.5%           | ~75%         | ~74%          | ~74%           |
| Simple-QA    | 55.2%           | ~58%         | ~56%          | ~57%           |
| STEM/Math    | Top-tier        | Top-tier     | Top-tier      | Top-tier       |

The improvements over V3.2 are significant:

| Benchmark     | V4 Pro | V3.2-Base | Improvement |
|--------------|--------|-----------|-------------|
| MMLU         | 90.1%  | 87.8%     | +2.3%       |
| MMLU-Pro     | 73.5%  | 65.5%     | +8.0%       |
| Simple-QA    | 55.2%  | 28.3%     | +26.9%      |
| HumanEval    | 76.8%  | 62.8%     | +14.0%      |
| LongBench-V2 | 51.5%  | 40.2%     | +11.3%      |

My takeaway: For knowledge-intensive tasks—documentation synthesis, research analysis, Q&A systems—V4 Pro is practically equivalent to the closed-source leaders. The gap is within measurement noise.

Coding: Near the Top

DeepSeek openly acknowledges their position:

“Agentic Coding model for internal employees, better experience than Sonnet 4.5, delivery quality near Opus 4.6 non-thinking mode, but still has gap with Opus 4.6 thinking mode.”

This is remarkably honest for a model release. Here’s the coding performance:

| Model                | HumanEval | Agentic Coding Quality |
|---------------------|-----------|------------------------|
| DeepSeek V4 Pro     | 76.8%     | Near Opus 4.6 non-thinking |
| DeepSeek V3.2-Base  | 62.8%     | Lagging                |
| Claude Sonnet 4.5   | ~75%      | Good                   |
| Claude Opus 4.6 Max | ~85%      | Excellent (thinking mode) |

V4 Pro regained the “national coding champion” status among Chinese models. One-shot code generation is strong. Complex agentic workflows—where the model plans, executes, and iterates—still favor Opus 4.6’s thinking mode.

DeepSeek V4 Benchmark Details

Arena Rankings: What Users Think

The LMSYS Arena provides real-world preference rankings:

| Arena           | DeepSeek V4 Pro Rank | Open-Source Leader |
|----------------|---------------------|-------------------|
| Text Arena     | 2nd (open-source)   | GLM-5.1 (1st)     |
| Code Arena     | 3rd (open-source)   | GLM-5.1 (1st), Kimi K2.6 (2nd) |

Interestingly, V4 Pro leads other open-source models on world knowledge tests, only slightly behind Gemini Pro 3.1 among all models.

Why This Matters for Your Stack

I built a simple decision matrix for choosing between these models:

# Performance tier mapping for decision-making
MODEL_PERFORMANCE_TIERS = {
    "reasoning_knowledge": {
        "tier_1": ["Opus 4.6 Max", "GPT-5.4 xHigh", "Gemini 3.1 Pro"],
        "tier_1_5": ["DeepSeek V4 Pro"],  # Competitive, slight gap
        "tier_2": ["GLM-5.1", "Kimi K2.6", "DeepSeek V3.2"]
    },
    "agentic_tasks": {
        "tier_1": ["Opus 4.6 Thinking", "GPT-5.5"],
        "tier_2": ["Opus 4.6 Non-thinking", "DeepSeek V4 Pro"],
        "tier_3": ["Sonnet 4.5", "DeepSeek V3.2"]
    }
}

# Cost comparison (per 1M input tokens, estimated)
API_COST_COMPARISON = {
    "Opus 4.6 Max": "$15",
    "GPT-5.4 xHigh": "$10",
    "DeepSeek V4 Pro API": "$2-3",  # Significant savings
    "DeepSeek V4 Pro Self-hosted": "$0.50 hardware cost"  # Even cheaper
}

def select_model(task_type: str, budget_tier: str, data_sensitivity: str) -> str:
    """Select the best model for your use case."""
    if task_type == "complex_agent":
        if budget_tier == "unlimited":
            return "Opus 4.6 Max (thinking mode)"
        else:
            return "DeepSeek V4 Pro + Opus fallback for edge cases"

    if data_sensitivity == "high":
        return "DeepSeek V4 Pro (self-hosted)"

    if budget_tier in ["low", "medium"]:
        return "DeepSeek V4 Pro API"

    return "Opus 4.6 Max or GPT-5.4 xHigh"

When to Use V4 Pro

Go with DeepSeek V4 Pro if:

You need knowledge extraction or document analysis
Single-pass code generation is your main use case
Cost sensitivity is a factor (5-7x cheaper than Opus)
Data sovereignty requires on-premise deployment
You need to run on Huawei Ascend chips (US hardware restrictions)

Stick with Opus/GPT if:

Complex multi-step agents are your core workload
Maximum reasoning depth is non-negotiable
API convenience outweighs cost savings
You need the absolute best agentic performance

The Enterprise Angle

For enterprises, V4 Pro offers something unique: a viable open-source alternative for most production workloads with full control over data and deployment. The MIT license removes legal barriers, and self-hosting means zero marginal API cost beyond hardware.

For Chinese companies specifically, V4 Pro runs on Huawei Ascend chips, avoiding US hardware restrictions entirely. This domestic hardware support could be decisive for certain organizations.

Practical Recommendations

After analyzing the benchmarks, here’s my decision framework:

| Use Case                          | Recommended Model       | Rationale                        |
|----------------------------------|------------------------|----------------------------------|
| Knowledge extraction & Q&A       | DeepSeek V4 Pro        | Equivalent performance, lower cost |
| Document analysis & summarization | DeepSeek V4 Pro        | Strong world knowledge          |
| Code generation (one-shot)       | DeepSeek V4 Pro        | Near Sonnet quality             |
| Complex multi-step agents        | Opus 4.6 Max           | Thinking mode still superior    |
| Cost-sensitive production apps   | DeepSeek V4 Pro        | 5-7x cost reduction             |
| High-security on-premise         | DeepSeek V4 Pro        | MIT license, self-hostable      |

Final Thoughts

DeepSeek V4 Pro doesn’t beat Opus 4.6 Max on every benchmark. But it doesn’t need to. What it offers is competitive performance on most practical tasks at a fraction of the cost, with the option to self-host. The gap on agentic tasks is real, but for knowledge, reasoning, and coding workloads, V4 Pro is a legitimate alternative.

The honest self-assessment from DeepSeek about their model’s limitations is refreshing. It suggests a company focused on delivering real value rather than hype. For developers making architectural decisions, that transparency is valuable—it means you can trust the benchmarks and make informed trade-offs.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!