Abstract
DeepSeek-V4-Pro was released by DeepSeek-AI on April 24, 2026. It is positioned as a flagship Mixture-of-Experts reasoning model in the DeepSeek product line. The model uses a 1.6 trillion-parameter MoE architecture, with 49 billion activated parameters per inference pass. It also supports a native 1,048,576-token context window and a maximum single-turn output limit of 384,000 tokens.
This model is designed for code generation, advanced mathematical reasoning, and automated agent workflows. Its strengths are especially visible in competitive programming, symbolic reasoning, long-context code analysis, and tool-augmented execution.
This article analyzes DeepSeek-V4-Pro using standardized benchmark data from DataLearner’s 2026 evaluation library. It compares the model horizontally against GLM 5.1 and Kimi K2.6, and also tracks its vertical progress against earlier DeepSeek V3-series models. All benchmark scores and pricing figures are retained from the original testing records. The wording and structure have been rewritten with an independent analytical framework to improve readability and avoid duplicated phrasing.
1. Core Technical Architecture and Fundamental Specifications
DeepSeek-V4-Pro adopts a refined Mixture-of-Experts architecture. Compared with earlier DeepSeek V3 models, it uses a larger expert pool and more efficient inference routing. It is also paired with optimized hybrid attention mechanisms, including CSA and HCA. These mechanisms reduce inference overhead when processing million-token context inputs.
The model’s key specifications are summarized below.
Core Model Configuration
- Developer: DeepSeek-AI
- Release Date: April 24, 2026
- Knowledge Cutoff: May 2025
- Architecture: Mixture of Experts
- Expert Structure: 384 routing experts + 1 shared expert
- Total Parameters: 1.6T
- Activated Parameters per Inference Pass: 49B
- Native Context Window: 1,048,576 tokens
- Maximum Single-Turn Output: 384,000 tokens
- Supported Modes: Regular reasoning, High thinking, Max thinking
- Modality: Text-only
- Built-in Tool Support: Web retrieval and terminal execution
The 1M-token context window is one of the model’s most important upgrades. It represents a 4x to 8x expansion over DeepSeek V3.2’s 256K-token limit. This enables the model to process large technical documents, monorepo source code, and multi-chapter academic papers without heavy chunking.
For engineering teams, this matters in real workflows. Large repositories often lose structural coherence when split into many small context blocks. DeepSeek-V4-Pro reduces that problem by keeping more source files, dependency paths, and technical documents in one reasoning session.
When combined with tool-augmented reasoning modes, the model shows clear gains in coding, mathematics, and agent automation benchmarks. In production environments, unified request dispatch through platforms such as 4sapi can also simplify model switching, especially when teams need to compare DeepSeek-V4-Pro with GLM, Kimi, or other LLM endpoints without rewriting authentication and routing logic.
2. Horizontal Benchmark Performance vs GLM 5.1 and Kimi K2.6
The following comparison uses each model’s strongest available configuration. This includes maximum reasoning mode and tool-augmented execution where applicable.
The benchmarks cover six major categories:
- Competitive coding
- Real-world software engineering
- Mathematical olympiad reasoning
- Graduate-level scientific knowledge
- Cross-domain expert knowledge
- AI agent tool utilization
The benchmark definitions follow common 2026 LLM evaluation standards.
- LiveCodeBench: A contamination-resistant competitive programming benchmark using post-training-cutoff problems.
- Codeforces: A rating-style benchmark aligned with human competitive programming difficulty.
- SWE-bench Series: Real-world GitHub issue resolution tasks across verified, multilingual, and high-difficulty repositories.
- IMO-AnswerBench: International Mathematical Olympiad-style problems for multi-step deduction.
- GPQA Diamond: Graduate-level STEM reasoning and knowledge evaluation.
- HLE: Humanity’s Last Exam, an ultra-hard cross-disciplinary reasoning benchmark.
- BrowseComp: Web retrieval and browsing-agent evaluation.
- Terminal Bench 2.0: Shell command, scripting, and terminal task execution benchmark.
2.1 Coding and Competitive Programming: DeepSeek-V4-Pro Takes a Clear Lead
DeepSeek-V4-Pro performs especially well in algorithmic coding and competitive programming. Its advantage is visible in both LiveCodeBench and Codeforces.
On LiveCodeBench Max Thinking, DeepSeek-V4-Pro scores 93.50, ranking first among 120 tested models. This is nearly 4 points higher than Kimi K2.6’s 89.60. It also exceeds DeepSeek V3.2’s 83.30 by more than 10 points.
On Codeforces Max Rating, DeepSeek-V4-Pro reaches 3206, ranking second across 16 benchmarked models. Compared with DeepSeek V3.2’s 2386, this is an 820-point increase. The score places DeepSeek-V4-Pro in the upper tier of competitive programming models and close to elite human problem-solving levels.
However, the picture becomes more balanced in software engineering benchmarks.
- SWE-bench Verified: DeepSeek-V4-Pro 80.60 vs Kimi K2.6 80.20
- SWE-bench Multilingual: DeepSeek-V4-Pro 76.20 vs Kimi K2.6 76.70
- SWE-bench Pro: DeepSeek-V4-Pro 55.40, behind GLM 5.1 58.40 and Kimi K2.6 58.60
This split is important. DeepSeek-V4-Pro is excellent at generating new algorithmic logic. It is also strong at structured coding tasks. But it is slightly weaker when debugging complex, unfamiliar, or proprietary enterprise repositories.
In simple terms, DeepSeek-V4-Pro is better at “building logic” than “repairing messy legacy systems.”
2.2 Mathematical Reasoning: Top-Tier Domestic Model Performance
DeepSeek-V4-Pro also performs strongly in mathematical reasoning. Its advantage is most visible on olympiad-style deduction tasks.
On IMO-AnswerBench Max Score, DeepSeek-V4-Pro reaches 89.80 and ranks third among 20 tested models. GLM 5.1 scores 83.80, while Kimi K2.6 scores 86.00. This gives DeepSeek-V4-Pro a 6-point lead over GLM 5.1 and a 3.8-point lead over Kimi K2.6.
On GPQA Diamond Max Score, DeepSeek-V4-Pro records 90.10. This is close to Kimi K2.6’s 90.50 and clearly ahead of GLM 5.1’s 86.20.
The model also shows strong vertical progress. DeepSeek V3.2 scored 82.40 on GPQA Diamond, while V4-Pro reaches 90.10. This is an improvement of nearly 8 points. It suggests major progress in formal reasoning, symbolic calculation, and multi-step logic.
For use cases such as mathematical proof generation, quantitative modeling, and research-grade symbolic reasoning, DeepSeek-V4-Pro is one of the strongest options in its class.
2.3 Cross-Domain Knowledge: The Main Weakness
DeepSeek-V4-Pro is less dominant in broad cross-disciplinary reasoning. This is most visible on HLE, which tests difficult knowledge across multiple domains.
The scores are:
- DeepSeek-V4-Pro: 48.20
- GLM 5.1: 52.30
- Kimi K2.6: 54.00
DeepSeek-V4-Pro trails GLM 5.1 by 4.10 points and Kimi K2.6 by 5.80 points.
This does not mean DeepSeek-V4-Pro is weak overall. Its HLE performance has improved significantly compared with earlier DeepSeek generations. But it still falls behind competitors in tasks that require broad knowledge across humanities, niche sciences, historical domains, and interdisciplinary reasoning.
This limitation matters for general-purpose research assistants. If the task requires encyclopedic knowledge, high source diversity, or non-technical reasoning, DeepSeek-V4-Pro should be paired with retrieval tools or complemented by another model.
Its best role is not as a universal knowledge assistant. Its best role is as a technical reasoning engine.
2.4 AI Agent and Tool Utilization: Strong Gains in Retrieval and Terminal Workflows
DeepSeek-V4-Pro also performs well in agent-oriented benchmarks. These tests evaluate whether a model can use external tools across multi-step workflows.
On BrowseComp, which measures web information gathering, the results are very close:
- DeepSeek-V4-Pro: 83.40
- Kimi K2.6: 83.20
- GLM 5.1: 79.30
DeepSeek-V4-Pro has a small lead over Kimi K2.6 and a larger lead over GLM 5.1. The difference between DeepSeek and Kimi is small, so both are competitive for web retrieval workflows.
On Terminal Bench 2.0, the gap becomes clearer:
- DeepSeek-V4-Pro: 67.90
- Kimi K2.6: 66.70
- GLM 5.1: 63.50
DeepSeek-V4-Pro performs better in shell scripting, file manipulation, command sequencing, and terminal-based task execution.
The vertical improvement is more impressive. BrowseComp increased from DeepSeek V3.2’s 51.40 to 83.40, a gain of about 62%. Terminal Bench 2.0 increased from 46.40 to 67.90, a gain of about 46%.
This shows that DeepSeek’s V4-Pro iteration focused heavily on agent execution. Tool call stability, multi-turn task state, and command-line reasoning all improved compared with the V3 series.
3. Vertical Performance Trend: DeepSeek-V4-Pro vs V3.2, V3.1, and R1-0528
The following table tracks DeepSeek’s progress across four model generations.
| Benchmark Metric | DeepSeek-V4-Pro | DeepSeek V3.2 | DeepSeek V3.1 | DeepSeek R1-0528 |
|---|---|---|---|---|
| LiveCodeBench Max | 93.50 | N/A | 74.80 | 73.30 |
| SWE-bench Verified Max | 80.60 | 73.10 | 66.00 | 57.60 |
| GPQA Diamond Max | 90.10 | N/A | 80.10 | 81.00 |
| HLE Max + Tools | 48.20 | N/A | 15.90 | 17.70 |
| Terminal Bench 2.0 Max | 67.90 | 46.40 | N/A | N/A |
Three trends are clear.
First, coding ability improved sharply. LiveCodeBench increased by nearly 20 points compared with V3.1 and R1-0528. This is the strongest signal that DeepSeek-V4-Pro was optimized for algorithmic code generation.
Second, cross-domain reasoning improved by a large percentage. HLE more than tripled compared with V3.1. However, its absolute score still trails GLM 5.1 and Kimi K2.6.
Third, agent tool execution became a clear upgrade area. Terminal Bench 2.0 increased from 46.40 to 67.90 compared with V3.2. This represents a 46% improvement in terminal task completion.
The data suggests a focused R&D direction. DeepSeek-V4-Pro prioritizes code, mathematics, and agent automation. Broad encyclopedic reasoning appears to be a secondary optimization target.
4. Standard API Token Pricing Comparison
The following pricing figures reflect standard published API rates in USD per 1 million tokens. Enterprise discounts and promotional pricing are excluded.
| Model | Input Cost / 1M Tokens | Output Cost / 1M Tokens | Cost Profile Breakdown |
|---|---|---|---|
| DeepSeek-V4-Pro | $0.435 | $0.87 | Low input and output cost; highly suitable for output-heavy tasks such as code generation and mathematical proof writing |
| GLM 5.1 | $1.40 | $4.40 | Moderate input price but high output cost; less economical for long-form generation |
| Kimi K2.6 | $0.95 | $4.00 | Lower input cost than GLM 5.1, but output cost remains high; better for short-response classification and tagging tasks |
DeepSeek-V4-Pro has the most attractive price profile among the three models. Its output cost is especially competitive. This matters because its strongest use cases are usually output-heavy.
Code generation, proof writing, shell automation, and technical reasoning often produce long responses. In these workloads, output token cost has a direct impact on monthly API spending.
Compared with GLM 5.1 and Kimi K2.6, DeepSeek-V4-Pro can reduce operational cost while still delivering stronger results in code and mathematical reasoning. This makes it suitable for continuous code generation pipelines, automated testing workflows, and quantitative research systems.
For mixed workloads, teams may still need multiple models. A practical stack can route code and math tasks to DeepSeek-V4-Pro, while assigning broad research or general knowledge tasks to Kimi K2.6 or GLM 5.1.
5. Strengths, Limitations, and Target Deployment Scenarios
5.1 Core Competitive Advantages
DeepSeek-V4-Pro has five major strengths.
First, it delivers industry-leading performance in competitive coding and algorithm generation. Its LiveCodeBench and Codeforces scores show clear advantages over earlier DeepSeek models and most peer alternatives.
Second, it offers strong mathematical reasoning. Its IMO-AnswerBench and GPQA Diamond results make it suitable for olympiad-style problems, graduate-level STEM reasoning, and symbolic math tasks.
Third, it improves agent tool calling. BrowseComp and Terminal Bench 2.0 show better retrieval behavior, stronger command-line reasoning, and more stable multi-step execution.
Fourth, its 1M-token native context window reduces the need for document chunking. This is valuable for large codebases, technical reports, academic papers, and long product specifications.
Fifth, its pricing is highly competitive for output-intensive workloads. Since code and mathematical outputs are often long, the lower output token price can create real cost savings at scale.
5.2 Key Limitations
DeepSeek-V4-Pro also has clear limitations.
First, it is weaker in broad cross-disciplinary knowledge reasoning. Its HLE score trails GLM 5.1 and Kimi K2.6. This makes it less suitable as a standalone general research assistant.
Second, it is not the strongest choice for difficult proprietary software debugging. On SWE-bench Pro, it falls behind GLM 5.1 and Kimi K2.6. Teams working with legacy enterprise systems may need additional validation.
Third, open-weight availability is limited. For teams that require self-hosting, private deployment, or full model-level control, this may restrict adoption.
These limitations do not reduce its value in its target areas. They simply define where it should and should not be used.
5.3 Recommended Business Use Cases
DeepSeek-V4-Pro is best suited for technical and reasoning-heavy workloads.
Recommended scenarios include:
- Competitive programming training platforms
- Automated code contest problem solvers
- Enterprise backend development workflows
- Large monorepo analysis and code generation
- Mathematical research and proof generation
- Financial quantitative modeling
- Symbolic computation automation
- DevOps agents for shell scripting and log analysis
- Multi-step terminal task orchestration
Alternative models may offer better ROI in other scenarios.
For broad academic or encyclopedic research, Kimi K2.6 may be a better choice. For complex legacy enterprise bug remediation, GLM 5.1 or Kimi K2.6 may perform better. For short-input and short-output classification tasks, Kimi K2.6 can be competitive due to its lower input cost compared with GLM 5.1.
The best deployment strategy is not to use one model for everything. It is to match each model to the workload where it performs best.
6. Final Comprehensive Conclusion
DeepSeek-V4-Pro has a clear position in the 2026 domestic flagship LLM landscape. It is not designed to be a general-purpose assistant for every task. Instead, it is a specialized model for code, mathematics, and tool-augmented agent automation.
Its strongest results appear in competitive programming and olympiad-level mathematical reasoning. These areas match the needs of engineering teams, quantitative researchers, algorithm platforms, and technical automation systems.
The model also shows strong vertical progress compared with earlier DeepSeek releases. Its improvements in LiveCodeBench, SWE-bench Verified, HLE, and Terminal Bench 2.0 indicate a focused iteration strategy. DeepSeek’s V4-Pro generation clearly prioritizes algorithmic logic, long-context reasoning, and agent tool execution.
From a cost perspective, DeepSeek-V4-Pro is especially attractive for output-heavy workloads. Its low output token price makes it practical for long code generation, mathematical proof writing, and multi-step shell automation. This cost structure strengthens its value in production environments where generation volume is high.
The main tradeoff is broad knowledge coverage. DeepSeek-V4-Pro still trails GLM 5.1 and Kimi K2.6 on HLE. It is also not the strongest model for high-complexity proprietary code debugging. These weaknesses are important, but they do not affect its core use cases.
A balanced multi-model strategy is the most practical approach. Teams can use DeepSeek-V4-Pro for code, mathematics, and agent automation. They can use Kimi K2.6 or GLM 5.1 for broader research and difficult enterprise debugging. This routing strategy improves both performance and cost efficiency.
Overall, DeepSeek-V4-Pro is one of the strongest domestic MoE models for technical development and quantitative agent pipelines as of mid-2026. Its value is clearest when the workload requires long-context code understanding, structured reasoning, and large volumes of technical output.
