Gemma 4 vs DeepSeek V4: Benchmarks, Cost, License (2026)

DeepSeek's V4 release in early 2026 raised the bar for coding-focused open models, but at the cost of enterprise-grade hardware requirements. Google's Gemma 4 takes the opposite approach — efficient models that run on what you already own. Here's how they actually compare for real work.

Quick Comparison

Short version: Gemma 4 fits on a workstation and speaks every language you'd need. DeepSeek V4 leads coding-specific benchmarks but demands a data-center to run locally.

Benchmark Deep Dive

Numbers from April 2026 leaderboards, FP16 where noted:

The honest read: DeepSeek V4 is noticeably better at coding tasks (HumanEval +7.3pt, SWE-bench +13.3pt). Outside coding, the two are within a few points of each other on most benchmarks. If coding isn't your primary workload, you're picking between models that score nearly the same on paper.

Language Coverage

This is where the gap widens:

• English: roughly tied
• Chinese: DeepSeek competitive (~84% on C-Eval vs Gemma 4 ~84%)
• Japanese (JGLUE): Gemma 4 ~81%, DeepSeek ~66%
• Indonesian, Vietnamese, Thai, Hindi: Gemma 4 holds within ~5pt of English; DeepSeek drops 15–25pt
• European languages (fr/es/de): Gemma 4 consistent; DeepSeek drops 8–12pt

If your product ships outside China/US, Gemma 4 is a different class.

Hardware Requirements

Running Gemma 4

A single workstation handles everything up to 31B.

Running DeepSeek V4

DeepSeek V4 ships as a ~685B parameter MoE (37B active per token). That headline "37B active" number makes it sound cheap to run — it isn't. You still need to keep the entire weight set in memory:

• Minimum self-host: 8× A100 80GB (640 GB VRAM), FP8 quantization
• Recommended production: 16× H100 80GB
• Quantized to Q4: still needs ~4× A100 80GB
• Cloud monthly cost (own infra): $15k–25k
• On-prem initial: $300k+

Most teams will use the DeepSeek-hosted API rather than self-host.

Inference Speed

Same hardware (4× A100 80GB), both at Q4:

For smaller self-hosted setups, Gemma 4 at 31B on a single RTX 4090 hits ~35 tok/s. DeepSeek V4 simply doesn't run there.

Cost Comparison (1M requests/month workload)

Gemma 4 Self-Hosted

DeepSeek V4 via API

DeepSeek V4 Self-Hosted

For any sustained workload below millions of daily requests, Gemma 4 self-hosted wins by 10–50×.

When to Pick Which

Pick Gemma 4 if:

• You deploy on anything less than an 8-GPU cluster
• You need Apache 2.0 (no commercial license questions)
• Your users speak anything beyond English and Chinese
• You need multimodal inputs (image, audio, video)
• Per-dollar quality matters

Pick DeepSeek V4 if:

• Coding / SWE-bench accuracy is your primary metric
• You're OK with API costs or have the multi-GPU infra
• English/Chinese-only workload
• You need the absolute best numbers on HumanEval and SWE-bench

Deployment

Gemma 4 via Ollama

ollama pull gemma4:31b
ollama run gemma4:31b

For edge devices, see Gemma 4 mobile deployment.

DeepSeek V4 via API

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_KEY",
    base_url="https://api.deepseek.com/v1"
)

resp = client.chat.completions.create(
    model="deepseek-v4",
    messages=[{"role": "user", "content": "..."}]
)

Self-hosting DeepSeek V4 requires a full vLLM + multi-GPU setup well outside the scope of a blog post.

Migration Notes

From DeepSeek V4 API → Gemma 4 self-hosted: Swap the API client for Ollama or vLLM. Prompts generally transfer. Expect coding tasks to need 2–5% more iteration; everything else should be roughly equivalent.

Fine-tunes: DeepSeek V4 fine-tunes are license-restricted. Gemma 4 fine-tunes are yours under Apache 2.0. If you have a DeepSeek fine-tune you care about, budget 1–2 weeks to retrain equivalent on Gemma 4.

FAQ

Is DeepSeek V4 really that much better at coding?

On HumanEval (single-function, well-defined problems) and SWE-bench (real PR-level refactors), yes — measurably. For everyday "write me this React component" or "fix this SQL query" work, the gap disappears. If your metric is closing GitHub issues end-to-end, DeepSeek leads. If it's developer productivity day-to-day, Gemma 4 31B is usually good enough.

Can I run DeepSeek V4 on a MacBook?

No. Even the Q4 quantized variant needs ~220 GB of memory. Apple Silicon tops out at 192 GB unified memory (M3 Ultra), and you'd still hit compatibility issues. Gemma 4 26B/31B runs fine on an M2 Max or M3 Pro with 32–64 GB.

Which handles Chinese better?

Roughly tied on C-Eval and CMMLU. DeepSeek was Chinese-first in design; Gemma 4 is multilingual-first. For pure Chinese NLP tasks, pick either based on other criteria (cost, license, deployment) — performance is close.

What about commercial use?

Gemma 4 is Apache 2.0 — commercial use with no restrictions. DeepSeek V4 has a custom license that restricts certain use cases and requires review for some commercial deployments. Check the exact terms if you're building a product around it.

Is DeepSeek V4 open-weights?

Yes, weights are published on Hugging Face. But "open weights" ≠ "practical to self-host" when the model needs 8× A100s. For most teams, DeepSeek V4 is effectively an API product even though the weights are public.

Which is better for reasoning tasks?

DeepSeek V4 edges Gemma 4 on math (MATH 71.8% vs 68.5%). Gemma 4 leads on GPQA Diamond (62.1% vs 59.4%) and TruthfulQA (68.9% vs 66.2%). Neither is decisively better at reasoning overall.

Will Gemma 4 get a coding-specialized variant?

Google hasn't announced one publicly. If/when they ship a Gemma 4 Code variant, the HumanEval gap with DeepSeek would likely close. Base Gemma 4's 82.7% HumanEval is already above every previous open model except DeepSeek V4 and Llama 4.1 400B.

Related Comparisons

• Gemma 4 vs Llama 4.1 — the other hot open model of April 2026
• Gemma 4 vs GPT-4 — open vs the OpenAI baseline
• Gemma 4 vs Claude 3.5 — open vs Anthropic's flagship
• Gemma 4 vs Qwen 3 — the other strong multilingual open model
• Gemma 4 Benchmarks Full Breakdown — all the numbers in one place

Bottom Line

For most teams in April 2026, Gemma 4 is the practical choice. It runs where you want it to run, speaks the languages your users speak, ships under a license that doesn't need a lawyer, and costs 10–50× less over a year.

DeepSeek V4 is the right pick specifically when: you need top-tier coding benchmarks, you have the infrastructure (or the API budget), and your workload is English/Chinese-only. Outside that narrow window, you're paying a lot for a marginal benchmark edge.

Last updated: April 18, 2026. Benchmarks from official leaderboards and community reproductions.