Gemma 4 Performance Benchmark — Compared Against Llama 4, Qwen, Mistral, and DeepSeek on Quality, Speed, and Cost-Efficiency [2026 Open-Weights LLM Showdown]

As of May 2026, Gemma 4 lands as "top-tier quality at its size class, somewhat trailing on raw throughput, the most permissive license overall." Quick map of which model wins which axis:

If you need maximum total score per VRAM, certainty of commercial licensing, and edge-class deployment, Gemma 4 is the May 2026 default. For pure throughput on batch workloads or top-tier math / code reasoning, Mistral or DeepSeek may earn a slot in a hybrid stack.

This column is the sequel to Gemma 4 System Requirements and Gemma 4 + AI Studio Update, focused on quality, speed, and selection criteria with primary sources.

Open-weights, production-grade models in scope for the comparison:

For Gemma 4 we'll focus on E4B (4B-effective edge model), 26B MoE (≈4B active at inference), and 31B Dense (flagship).

Compiled from each vendor's announcements, model cards, and standard leaderboards (lmarena.ai, etc.) as of May 2026. Compare within size classes to be fair.

4B-class (edge / consumer GPU)

Dense 30B-class (business-grade local LLM)

Reading: at 30B class the four vendors are within 1–3 points — effectively a wash. Selection should hinge on throughput, memory footprint, license, and function-calling shape, which we cover next.

Important caveat: figures above are May-2026 vendor disclosures aggregated. Measurement conditions (few-shot, quantization, eval harness) vary. Always cross-check on the Open LLM Leaderboard or Chatbot Arena.

When quality is similar, throughput is the next deciding factor. Same VRAM, same quantization.

RTX 4090 (24GB) / Q4

Apple Silicon M3 Max 64GB / MLX / Q4

Reading: Mistral Small 3 has a slight throughput edge in 4B class. Gemma 4 26B MoE is the most interesting trade — 4B-effective compute serving 26B-class knowledge, well-balanced on memory and speed.

Best-LLM-per-VRAM-budget perspective. MMLU-Pro ÷ Q4 VRAM:

Reading: Gemma 4 E4B delivers ~60-something MMLU-Pro at just 3GB VRAM. Best quality-per-GB among open-weights LLMs in May 2026. For edge / mobile / low-spec deployment, it's the obvious starting point.

Self-hosted cost (GPU amortization + electricity), assuming an RTX 4090 rentable at ~¥5,000/month equivalent:

For reference: OpenAI GPT-4o API runs ~$2.50/1M input → ~$10.00/1M output = ¥375–¥1,500 per 1M. Self-hosting Gemma 4 E4B can deliver under 1/100 of the API price (quality gap is separate).

Evaluated on JGLUE / JCommonsenseQA and other public Japanese benchmarks:

Reading: For Japanese, Qwen 3.5 and Gemma 4 are the two front-runners. Mistral leans European, Llama 4 leans English. Combining Japanese performance + Apache 2.0 + multimodal, Gemma 4 is the current best all-rounder for Japanese enterprise use.

Critical for agent workflows: native tool-call shape and multi-step reasoning.

Gemma 4, Qwen 3.5, Mistral, and DeepSeek all ship native function calling — the easy quartet for agent integration. Llama 4 routes function calls through prompt templates, which complicates direct integration with MCP-based agents like Claude Code Agent View or Cursor Automations.

Open-weights labels mask large practical differences.

Apache 2.0 / MIT are the cleanest for commercial use. Gemma 4 / Mistral Small-Medium / Phi-5 sit in the safe zone. Llama 4's MAU cap and Meta-competing clause add legal review overhead for global SaaS and financial products.

Six common deployment shapes, May 2026:

- Throughput is not best-in-class — Mistral / Qwen are 10–20% faster at the same size; matters for batch - Coding-specialized leaderboards — Qwen Coder series still tops HumanEval / MBPP - Long-context (128K+) degradation — solid through ~32K, weaker than peers beyond that (third-party reports) - All numbers are May 2026 snapshots — refresh every 2–3 months

What we default to inside our AI consulting practice for Japanese-enterprise in-house LLM and edge AI engagements:

1. PoC / dev: Gemma 4 E4B + Ollama for local validation → Gemma 4 31B Dense + vLLM for production-scale evaluation 2. Production / in-house LLM: Gemma 4 31B Dense (Japanese business), or Qwen 3.5 32B when pure Japanese quality is essential 3. Edge / mobile: Gemma 4 E4B (including Argent × Gemma 4 iOS automation patterns) 4. Agent platform: Gemma 4 + MCP, compatible with Claude Code Agent View and Cursor Automations 5. Aggressive cost compression: self-hosted designs that reach <1/100 of API pricing through dedicated-GPU amortization

Q1. Gemma 4 vs Llama 4 — which? A. Gemma 4 wins on license freedom, Japanese quality, and multimodal. Llama 4 carries an MAU cap and a Meta-competing clause, so global SaaS gets extra legal review. Pick Llama 4 only for English-leaning research use cases. Q2. Japanese business — Qwen 3.5 or Gemma 4? A. Qwen 3.5 edges out on JCommonsenseQA / JGLUE, but Gemma 4's multimodal + Apache 2.0 + Vertex AI / AI Studio ecosystem tilt the overall call toward Gemma 4. Q3. What runs on an RTX 3060 (12GB)? A. Comfortable: Gemma 4 E4B Q4 (3GB), Qwen 3.5 4B, Phi-5 14B Q4, Mistral Small 3 Q4. Tight: 26B MoE Q4 (10GB). 31B Dense is out. See Gemma 4 System Requirements. Q4. Why don't benchmark scores match real-world quality? A. MMLU-Pro / HumanEval test general knowledge and code generation; real workflows (internal Q&A, email drafting, summarization) hinge more on fine-tuning, RAG quality, and prompt engineering. Treat benchmarks as a floor check; A/B test on your own data. Q5. What's coming in late 2026? A. Google has Gemini 3.5 Pro slated for June 2026; Meta is preparing reasoning-focused Llama 4 variants; Alibaba has hinted at Qwen 4. Plan for a six-month re-evaluation cycle, with an architecture that swaps models cleanly. Q6. Anything to watch out for with Gemma 4 commercial use? A. Apache 2.0 is extremely permissive, but Gemma's Prohibited Use Policy still bans weapons, CSAM, and similar use. Normal business use has no practical issues.

For the May 2026 trifecta of best total quality per VRAM × Apache 2.0 × native function calling × multimodal × strong Japanese, Gemma 4 is the current default open-weights choice.

But it doesn't win every axis. The realistic answer is hybrid: Gemma 4 as the default, with Mistral (speed), Qwen (Japanese-pure), DeepSeek (math), and Llama 4 (English research) added as needed. We typically benchmark with the customer's own data inside an AI consulting engagement before locking the production stack.

Primary: - Gemma 4 model card (Hugging Face) - Android Developers Blog — Gemma 4 announcement - Google Developers Blog — Gemma 4 agentic skills - Meta — Llama 4 - Qwen docs - Mistral - DeepSeek - Gemma Prohibited Use Policy Benchmarks: - Open LLM Leaderboard (Hugging Face) - Chatbot Arena (lmarena.ai) - JGLUE / JCommonsenseQA Related columns: - Gemma 4 System Requirements - Gemma 4 + Google AI Studio update - Gemini 3.5 Flash + Omni - Argent × Gemma 4 — on-device AI agent - Qwen 3.5 9B Complete Guide Note: All benchmark figures are May-2026 snapshots aggregated from vendor disclosures and third-party leaderboards. Measurement conditions (few-shot, quantization, eval harness) vary, so confirm with your own A/B tests. Vendor releases land roughly quarterly — re-validate every 2–3 months.