TL;DR
- V4 Pro: 1.6T total parameters, 49B active (MoE) — $0.43/M input, $1.74/M output — stronger reasoning
- V4 Flash: 284B total parameters, 13B active (MoE) — $0.09/M input, $0.36/M output — 4.8× cheaper
- 1M token context window on both variants
- Flash maintains ~80-85% of Pro's quality at 21% of the cost
- Verdict: Use Flash for high-volume, latency-sensitive, simpler tasks. Use Pro for complex reasoning, coding, and agentic workflows
- Both are open-weight (MIT license), released April 24, 2026
The Two Variants, Side by Side
DeepSeek broke the open-source model market on April 24, 2026 by releasing not one but two variants simultaneously — a strategy that forced every other lab to answer the same question: do you need the full Pro, or will Flash do?
| Spec | V4 Pro | V4 Flash |
|---|---|---|
| Total parameters | 1.6T | 284B |
| Active parameters | 49B (3.1%) | 13B (4.6%) |
| Architecture | MoE (256 experts, 8 active) | MoE (64 experts, 8 active) |
| Context window | 1M tokens | 1M tokens |
| Input pricing | $0.43 / M tokens | $0.09 / M tokens |
| Output pricing | $1.74 / M tokens | $0.36 / M tokens |
| Cached input | $0.08 / M tokens | $0.02 / M tokens |
| License | MIT | MIT |
| Release date | April 24, 2026 | April 24, 2026 |
(Source: DeepSeek official, BenchLM comparison)
Benchmark Results Across 22 Categories
Independent benchmark data from multiple evaluators tells a consistent story. Here are the key results across major categories:
1. General Knowledge & Reasoning
| Benchmark | V4 Pro | V4 Flash | Δ |
|---|---|---|---|
| MMLU-Pro | 85.3% | 82.1% | -3.2 |
| GPQA Diamond | 71.8% | 66.4% | -5.4 |
| BBH (BigBench) | 91.2% | 87.6% | -3.6 |
| ARC-Challenge | 96.1% | 94.8% | -1.3 |
2. Coding
| Benchmark | V4 Pro | V4 Flash | Δ |
|---|---|---|---|
| HumanEval | 92.7% | 89.4% | -3.3 |
| MBPP | 88.5% | 85.1% | -3.4 |
| SWE-Bench Verified | 62.3% | 52.1% | -10.2 |
| LiveCodeBench | 67.8% | 56.4% | -11.4 |
3. Math
| Benchmark | V4 Pro | V4 Flash | Δ |
|---|---|---|---|
| MATH-500 | 96.2% | 92.8% | -3.4 |
| AIME 2024 | 76.8% | 63.5% | -13.3 |
| GSM8K | 96.8% | 95.1% | -1.7 |
4. Agentic & Tool Use
| Benchmark | V4 Pro | V4 Flash | Δ |
|---|---|---|---|
| BFCL (Tool Use) | 85.2% | 78.9% | -6.3 |
| AgentBench | 72.4% | 62.3% | -10.1 |
| Terminal-Bench | 76.2% | 64.8% | -11.4 |
(Sources: BenchLM, DeepSeek V4 DataCamp, 1min.AI)
The Pattern: Flash Loses Ground on Multi-Step Reasoning
The data reveals a clear pattern. On short-form benchmarks (MMLU-Pro, HumanEval, GSM8K), Flash trails Pro by 1-3 percentage points — barely noticeable in practice. On multi-step reasoning tasks that require deeper chains (AIME, SWE-Bench, AgentBench, Terminal-Bench), the gap widens to 10-13 points.
This makes intuitive sense given the architecture. Flash has 13B active parameters versus Pro's 49B. For a single token prediction on a simple question, that difference is manageable. But as the reasoning chain lengthens and the model needs to maintain state across multiple tool calls or mathematical derivations, the smaller active parameter count becomes a bottleneck.
Pro also has 256 total experts (vs 64 for Flash), giving it a larger pool of specialized knowledge to draw from for diverse tasks. Only 8 are active per token in both variants, but Pro's expert pool is 4× deeper, meaning the odds of having a specialist expert for any given sub-task are higher.
Pricing in Practice
The raw pricing difference is dramatic:
- Flash: $0.09/M input → 1M input tokens = $0.09
- Pro: $0.43/M input → 1M input tokens = $0.43
But the effective cost per completed task depends on what you're doing:
| Use Case | Flash cost/task | Pro cost/task | Best pick |
|---|---|---|---|
| Simple Q&A classification | $0.001 | $0.004 | Flash |
| Code completion (short) | $0.005 | $0.02 | Flash |
| Code review (complex PR) | $0.04 | $0.15 | Flash (if quality acceptable) |
| SWE-Bench task | $0.50 | $2.00 | Pro (10%+ better pass rate) |
| Math competition problem | $0.03 | $0.12 | Pro (13% better on AIME) |
| Agentic workflow (20+ steps) | $0.30 | $1.00 | Pro (reliability matters more) |
(Estimated per-task costs based on typical token consumption)
When to Use Which
The decision is not binary. Many production setups use both: Flash for the hot path, Pro for the hard cases:
Use DeepSeek V4 Flash when:
- High volume: Customer-facing chatbots, classification, embedding-style tasks
- Latency-sensitive: Flash is faster (13B active vs 49B) and cheaper per call
- Simple reasoning: Factual Q&A, summarization, translation, data extraction
- Cost-constrained startups: At $0.09/M input, Flash is competitive with GPT-4o mini pricing
Use DeepSeek V4 Pro when:
- Complex coding: Multi-file refactoring, test generation, code review
- Math and science: Competition-level problems, theorem proving, multi-step derivations
- Agentic workflows: Tool-using agents that need reliable multi-step reasoning
- Anything with long chains: The 10-13% gap on multi-step tasks is real
- Production agents: When a single failure costs more than the API call savings
How They Stack Against the Competition
| Model | Input cost / M | Output cost / M | SWE-Bench Verified | MMLU-Pro |
|---|---|---|---|---|
| DeepSeek V4 Flash | $0.09 | $0.36 | 52.1% | 82.1% |
| DeepSeek V4 Pro | $0.43 | $1.74 | 62.3% | 85.3% |
| Claude Opus 4.8 | $5.00 | $25.00 | 69.2% | 87.5% |
| GPT-5.5 | $5.00 | $30.00 | ~65% | 86.0% |
| Grok 4.5 | $2.00 | $6.00 | 61.4% | 83.0% |
Flash is in a league of its own on price. The closest competitor in its cost bracket — GPT-4o mini at $0.15/M input — scores roughly 15 points lower on MMLU-Pro and lacks the 1M context window. Pro competes in the same tier as Grok 4.5 on price but trails on top-end benchmarks.
FAQ
Which is better for coding? — Pro, especially for complex multi-file tasks. The 10% gap on SWE-Bench Verified is significant. For simple code completion, Flash is fine.
Can I use Flash for agentic workflows? — Yes, but expect ~10% lower task completion rates on multi-step workflows. For production agents handling critical tasks, pay for Pro.
Are both open-source? — Yes, MIT license. Full weights are available on Hugging Face.
Is there a reasoning mode? — Yes, both support DeepSeek's think-in-prompt approach, but Pro's larger active parameter count gives it substantially better results on reasoning-heavy tasks.
What's the context window? — Both support 1M tokens. This is a key differentiator versus GPT-5.5 (128K) and Claude Opus 4.8 (200K).
Further Reading
- BenchLM — DeepSeek V4 Flash vs Pro comparison
- DataCamp — DeepSeek V4: Features, Benchmarks, Comparisons
- 1min.AI — DeepSeek V4 Pro vs Flash Comparison
- InsiderLLM — DeepSeek V4 Flash vs Pro Guide
- DeepSeek official
- TAR — Grok 4.5 Deep-Dive
Cet article a été initialement publié sur The Agent Report.
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