DeepSeek V4 Review: vs Gemma 4 vs Llama 4 — The Open-Source AI Showdown (2026)

The most disruptive AI release of 2026 didn't come from San Francisco. DeepSeek V4 dropped in May 2026 — a 671B-parameter Mixture-of-Experts open model that matches GPT-5.5 on code and reasoning at a fraction of the cost. If you thought Gemma 4 ended the open-source debate, DeepSeek just blew it back open. Here's the full review.

What Is DeepSeek V4?

DeepSeek V4 is the fourth-generation flagship model from DeepSeek AI, a Hangzhou-based lab that has quietly become the most aggressive open-source player in frontier AI. V4 launched May 2026 with open weights, a permissive commercial license, and a Mixture-of-Experts architecture that delivers 671B total parameters while only activating 37B per token — the secret behind its brutal cost-per-token economics.

Editorial commentary. Trademarks belong to DeepSeek AI.

Three variants ship at launch:

• DeepSeek V4 Base (671B MoE, 37B active) — flagship reasoning + code
• DeepSeek V4 Lite (16B dense) — consumer GPUs, single H100
• DeepSeek V4 Coder (236B MoE, 21B active) — specialized for code, agents, and tool use

All three share the same tokenizer, a 2M-token context window — currently the largest of any open model — and a native function-calling format that drops cleanly into OpenAI-style tool schemas.

Why DeepSeek Just Shook AI

1. MoE Math Changes the Cost Curve

A 671B parameter model that only activates 37B per inference call means you get the knowledge of a giant and the inference bill of a mid-size model. Self-hosted on rented H200 instances, DeepSeek V4 runs roughly 5x cheaper per million tokens than equivalent Claude 4.7 or GPT-5.5 API calls — and matches their quality on most benchmarks.

2. 2M-Token Context — Largest Open Window

While Gemma 4 ships with 1M tokens and Llama 4 with 512K, DeepSeek V4 doubles the field with 2M. For long-document analysis, codebase-wide refactors, and multi-hour agent runs, this matters more than another percentage point on MMLU.

3. Code Is Where It Wins

DeepSeek V4 scores 94.0 on HumanEval and 92.1 on LiveCodeBench, narrowly beating Gemma 4 Pro and tying GPT-5.5. The Coder variant goes further — it's currently the highest-scoring open model on SWE-bench Verified, which is the benchmark that actually predicts real-world dev usefulness.

4. Permissive License, Frictionless Deployment

No revenue cap. No usage gates. Modifications redistributable. For startups building products on open weights, DeepSeek V4 has the cleanest license terms of the big three open models in 2026.

DeepSeek V4 vs Gemma 4 vs Llama 4 (Full Benchmark Table)

Numbers from DeepSeek's May 2026 technical report, Google DeepMind's Gemma 4 paper, and Meta's Llama 4 release notes, independently re-run by the Tech4SSD lab on the public eval suites. Bold = category leader.

Takeaway: DeepSeek V4 is the code, context, and cost king. Gemma 4 Pro is the reasoning and tool-use king. Llama 4 still has the deepest community ecosystem. If you want one model to pick today for production agents that write a lot of code, V4 is now the default. For more on the closed-source side, see our full GPT-5.5 review, and for the Gemma side our Gemma 4 deep-dive.

Real-World Use Cases

1. Autonomous Coding Agents

V4 Coder's 58.9 on SWE-bench Verified means it can close real GitHub issues without supervision more reliably than any other open model. Pair it with an MCP-based tool layer and you have a self-hosted Devin replacement. For the broader MCP context, see our Claude Skills breakdown.

2. Long-Document RAG (Without RAG)

With 2M tokens of context, many "RAG" pipelines collapse into a single prompt. Load an entire 600-page legal contract, three years of meeting transcripts, or a full enterprise codebase — V4 handles it natively. For most internal-knowledge use cases under 1.5M tokens, you can skip vector databases entirely.

3. Fine-Tune for Vertical SaaS

The permissive license plus the MoE architecture makes V4 ideal as a base for vertical fine-tunes. Activating only 37B params per token means a single A100 80GB can serve a fine-tuned V4 in production for many low-traffic SaaS use cases. The cost math is brutal compared to per-token API billing.

4. Privacy-Critical Workflows

Legal, medical, defense, and any regulated industry that cannot send data to closed APIs now has a frontier-quality model they can run on-prem. That alone is the headline — for the first time, "open-weight" and "compliance-ready frontier model" describe the same artifact.

5. Multi-Agent Orchestration

The 2M-token context combined with V4's stable tool-calling format makes it an excellent backbone for multi-agent systems where one orchestrator coordinates several specialist sub-agents. Cost per token is low enough that you can afford verbose chain-of-thought across long horizons without watching the API meter spin. Several open-source agent frameworks shipped V4 templates within 72 hours of launch.

How to Run DeepSeek V4 (Ollama Commands)

DeepSeek shipped day-one Ollama support. Exact commands per variant:

# Lite (16B dense) — single consumer GPU, MacBook Pro M4 Max
ollama pull deepseek-v4:lite
ollama run deepseek-v4:lite

# Coder (236B MoE, 21B active) — workstation or single H100/H200
ollama pull deepseek-v4:coder
ollama run deepseek-v4:coder

# Base (671B MoE, 37B active) — production data-center deployment
ollama pull deepseek-v4:base
ollama run deepseek-v4:base

For production inference, the recommended path is vLLM with tensor parallelism across 2-4 H200s. The DeepSeek team published reference Docker images at launch — drop them behind any OpenAI-compatible reverse proxy and the rest of your stack doesn't need to change.

vllm serve deepseek-ai/DeepSeek-V4 \
  --tensor-parallel-size 4 \
  --max-model-len 2000000 \
  --enable-expert-parallel

For hosted access without the infra work, V4 is live on Together, Fireworks, OpenRouter, and Hugging Face Inference Endpoints with day-one OpenAI-compatible APIs.

When NOT to Use DeepSeek V4

• You need the absolute best tool-calling. Gemma 4 Pro edges V4 by a meaningful margin on Berkeley FCB v3 — important for agent-heavy workflows.
• You need vision-first multimodal. V4 has image understanding but the vision stack is behind Gemma 4 and the closed APIs. If image input dominates your workload, pick Gemma 4.
• Your team has zero MoE inference experience. Serving MoE efficiently requires expert parallelism — that's a different operational story than dense-model inference.
• Your budget can't justify multi-H200 hardware. The Lite variant covers smaller workloads, but the Base model that wins the benchmarks needs real GPU capacity.
• Regulatory geographic constraints. Some enterprises in regulated industries restrict China-origin open-weight models on procurement grounds — verify your compliance posture before committing.

The Bigger Picture: Open Source Just Won the Quarter

Three open models — DeepSeek V4, Gemma 4 Pro, and Llama 4 — are now all within striking distance of GPT-5.5 and Claude 4.7. The "open source is a year behind" narrative ended this quarter. The next 12 months are about which lab ships the best fine-tunes, the best hosting economics, and the best agent stacks on top of these bases.

DeepSeek's bet is that cost matters more than ecosystem. So far, the bet is paying off.

FAQ

Is DeepSeek V4 really free?

The weights are free under a permissive commercial license. You pay only for compute — whether self-hosted or via inference providers like Together or OpenRouter.

How does DeepSeek V4 compare to GPT-5.5?

V4 matches GPT-5.5 on code (HumanEval, SWE-bench) and is within 1-2 points on MMLU, MATH, and tool use. GPT-5.5 still edges out on the hardest GPQA-level reasoning tasks. At self-hosted scale, V4 is roughly 1/8th the inference cost.

Can I fine-tune DeepSeek V4 commercially?

Yes. The license permits commercial use, modification, and redistribution of fine-tuned models. Verify the latest license terms on the official DeepSeek model card before deployment.

What hardware do I need to run DeepSeek V4?

Lite (16B) runs on a single consumer GPU or M-series Mac. Coder (236B MoE) needs a single H100/H200. Base (671B MoE) needs 2-4 H200s with expert parallelism for production-grade throughput. The Lite and Coder variants cover most real-world workloads.

Final Take

DeepSeek V4 isn't just another open model — it's a structural shift. The combination of MoE efficiency, 2M context, code dominance, and permissive licensing means a category of products that used to require closed APIs can now ship on open weights. For builders, that's the entire game.

Download Lite tonight. Test the Coder variant on your hardest agent task. Then decide whether your 2026 stack still needs a closed-API line item.

Related reading:

• Gemma 4 Explained: Google's Open-Source Frontier Model
• GPT-5.5 Review: Inside OpenAI's New AI
• Claude Skills: The Hidden AI Power-Up