Qwen3.5-4B vs Gemma 4 E4B: A Detailed Benchmark Comparison

Executive Summary

In April 2026, the open-source LLM landscape saw major releases from both Alibaba (Qwen3.5) and Google (Gemma 4). At the 4B deployment class—critical for edge AI, local inference, and resource-constrained agent deployment—two models directly compete: Qwen3.5-4B and Gemma 4 E4B.

This analysis compares these models across official benchmarks, architecture, capabilities, and practical use cases. Key finding: Qwen3.5-4B leads on most official benchmarks (reasoning, coding, multilingual, instruction-following), while Gemma 4 E4B has architectural advantages in multimodal understanding and long-context reasoning. The choice depends on your workload and constraints.

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Model Specs Comparison

Size & Architecture

Aspect	Qwen3.5-4B	Gemma 4 E4B
Nominal Parameters	4B	4B
Effective Parameters	4B	~1.0B (Mixture-of-Experts)
Loaded (with embeddings)	~4B	~8B
Architecture	Dense Transformer + Gated DeltaNet	Transformer (Hybrid Attention)
Layers	32	—
Hidden Dimension	2560	—
Vocab Size	248,320	262,144
Context Window (native)	262,144 tokens	128,000 tokens
Context (extensible)	Up to 1,010,000	—
Training Data Cutoff	Unknown (recent)	January 2025

Key Architecture Differences

Qwen3.5-4B:

• Dense transformer with Gated DeltaNet linear attention heads
• 32 layers with mixed attention pattern (3× DeltaNet → 1× Gated Attention)
• 262K native context, extensible to 1M+
• Multi-Token Prediction (MTP) support
• Thinking mode by default (extended reasoning, internal monologue)
• 201 languages native support

Gemma 4 E4B:

• Sparse Mixture-of-Experts (MoE) architecture
• ~1B active parameters per token (4.5B effective out of 8B loaded)
• Hybrid attention (local sliding-window + global)
• Proportional RoPE for long-context scaling
• 128K native context window
• Multimodal ready (text, image, audio inputs)
• 140+ languages

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Benchmark Comparison (Official, Shared Tests)

Knowledge & Reasoning (STEM)

Benchmark	Qwen3.5-4B	Gemma 4 E4B	Winner	Margin
MMLU-Pro	79.1%	69.4%	Qwen	+9.7pp
MMLU-Redux	88.8%	—	Qwen	—
C-Eval	85.1%	—	Qwen	—
SuperGPQA	52.9%	58.6%	Gemma	+5.7pp
GPQA Diamond	76.2%	58.6%	Qwen	+17.6pp

Verdict: Qwen leads decisively on general knowledge (MMLU-Pro, C-Eval). Gemma shows strength on graduate-level science questions (GPQA).

Instruction Following & Compliance

Benchmark	Qwen3.5-4B	Gemma 4 E4B	Winner	Margin
IFEval	89.8%	—	Qwen	—
IFBench	59.2%	—	Qwen	—
MultiChallenge	49.0%	—	Qwen	—

Verdict: Qwen excels at instruction-following tasks. Gemma doesn't report these benchmarks officially.

Long-Context Performance

Benchmark	Qwen3.5-4B	Gemma 4 E4B	Winner	Margin
AA-LCR	57.0%	—	Qwen	—
LongBench v2	50.0%	—	Qwen	—

Key Difference:

• Qwen: 262K native context (1M+ extensible) = can process large documents, books, logs without truncation
• Gemma: 128K native context = still substantial, but half of Qwen's native window

Verdict: Qwen wins on long-context capability.

Reasoning & Coding

Benchmark	Qwen3.5-4B	Gemma 4 E4B	Winner	Margin
HMMT Feb 25	74.0%	—	Qwen	—
HMMT Nov 25	76.8%	—	Qwen	—
LiveCodeBench v6	55.8%	52.0%	Qwen	+3.8pp
OJBench	24.1%	—	Qwen	—

Verdict: Qwen leads on competitive math and coding tasks.

Agent Capabilities & Tool Use

Benchmark	Qwen3.5-4B	Gemma 4 E4B	Winner	Margin
TAU2-Bench	79.9%	—	Qwen	—
BFCL-V4	50.3%	—	Qwen	—
VITA-Bench	22.0%	—	Qwen	—
DeepPlanning	17.6%	—	Qwen	—

Note: TAU2-Bench is critical for agentic workflows. Qwen's 79.9% is strong for a 4B model. Gemma doesn't publish these metrics for E4B.

Multilingual Performance

Benchmark	Qwen3.5-4B	Gemma 4 E4B	Winner	Margin
MMMLU	76.1%	76.6%	Gemma	+0.5pp
MMMLU-ProX	71.5%	—	Qwen	—
NOVA-63	54.3%	—	Qwen	—
INCLUDE	71.0%	—	Qwen	—
Global PIQA	78.9%	—	Qwen	—
WMT24++	66.6%	—	Qwen	—

Verdict: Roughly tied on single-benchmark MMMLU, but Qwen publishes more multilingual tests (201 languages trained vs. Gemma's 140+).

Vision & Multimodal (Image Understanding)

Benchmark	Qwen3.5-4B	Gemma 4 E4B	Winner	Margin
MMMU	77.6%	—	Qwen	—
MMMU-Pro	66.3%	52.6%	Qwen	+13.7pp
MathVision	74.6%	59.5%	Qwen	+15.1pp
Mathvista(mini)	85.1%	—	Qwen	—
OmniDocBench 1.5	86.2%	0.181 (edit dist)	Qwen	Better
MMBench EN-DEV v1.1	89.4%	—	Qwen	—
CountBench	96.3%	—	Qwen	—

Verdict: Qwen3.5-4B dominates multimodal reasoning despite both being multimodal-capable. Qwen's training on diverse visual content (diagrams, math, documents) shows clear advantage.

Video Understanding

Benchmark	Qwen3.5-4B	Gemma 4 E4B	Winner	Margin
VideoMME (w subs)	83.5%	—	Qwen	—
VideoMME (w/o subs)	76.9%	—	Qwen	—
VideoMMMU	74.1%	—	Qwen	—
MLVU	82.8%	—	Qwen	—

Verdict: Qwen has strong video understanding. Gemma 4 E4B doesn't officially report video benchmarks.

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Summary Scorecard (Official Benchmark Overlap)

Category	Qwen3.5-4B	Gemma 4 E4B	Note
General Knowledge	WIN	9.7pp	MMLU-Pro: 79.1% vs 69.4%
Long Context	WIN	2× larger (262K vs 128K)	Qwen: extensible to 1M+
Coding & Math	WIN	~4pp avg	LiveCodeBench, HMMT
Reasoning	WIN	—	GPQA Diamond: 76.2% vs 58.6%
Instruction Following	WIN	—	IFEval, IFBench (Gemma doesn't report)
Agentic Workflows	WIN	—	TAU2-Bench: 79.9% (Gemma no data)
Multimodal Vision	WIN	13-15pp	MMMU-Pro, MathVision
Video Understanding	WIN	—	(Gemma doesn't report)
Multilingual	TIE	~0.5pp	Both excellent, Qwen: 201 langs
Official Benchmark Count	30+	~15	Qwen publishes more tests for 4B

Clear Winner on Official Benchmarks: Qwen3.5-4B (wins ~80% of comparable tests)

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Third-Party Chat Preference (Arena AI)

While official benchmarks favor Qwen, third-party chat leaderboards tell a different story:

Arena AI Open-Source Text Leaderboard (March 31, 2026)

Model	Arena Score	Rank	Note
Gemma 4 31B	1452 ± 9	#3	Dense, best in class
Qwen3.5-397B-A17B	1449 ± 6	#4	Massive MoE
Gemma 4 26B A4B	1441 ± 9	#6	Efficient MoE
Qwen3.5-122B-A10B	1416 ± 6	—	Large MoE
Qwen3.5-27B	1404 ± 6	—	Dense
Qwen3.5-35B-A3B	1400 ± 6	—	MoE

Note: No direct 4B-vs-4B Arena AI comparison yet. Arena leaderboard reflects human chat preference, not static benchmark accuracy.

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Inference & Deployment

VRAM & Hardware Requirements

Model	Framework	Config	VRAM	Hardware
Qwen3.5-4B	vLLM	262K context	~8-12 GB	Consumer GPU (RTX 4060, M1/M2 with offload)
Gemma 4 E4B	vLLM	128K context	~6-10 GB	Lighter due to MoE sparsity
Gemma 4 E4B	SGLang	128K context	~6-8 GB	Optimized for tool-use

Verdict: Gemma E4B slightly lighter in practice (MoE activation efficiency), but Qwen's 262K context needs more KV cache planning.

Serving Frameworks

Qwen3.5-4B:

• vLLM, SGLang, KTransformers, Hugging Face Transformers
• Multi-Token Prediction (MTP) support for faster generation
• Thinking mode built-in (extended reasoning)

Gemma 4 E4B:

• vLLM, SGLang, KTransformers
• Tool-calling support (native function schema)
• Streamlined for multimodal (image/audio inputs)

Speed & Latency

Expected Inference Speed (per token, on H100):

• Qwen3.5-4B: ~15-20 ms/token (thinking mode adds overhead)
• Gemma 4 E4B: ~12-18 ms/token (sparse routing overhead < token generation gains)

Throughput: Both comparable, ~50-100 tokens/sec on consumer GPU with batching.

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Training Data & Alignment

Qwen3.5-4B

• Training Data:

  • Large-scale multimodal corpus (text, code, images, audio)
  • Web documents, code repositories, visual content
  • Training cutoff: Unknown (likely mid-2025)
  • 201 languages and dialects
  • Filtered for quality, safety, cultural sensitivity

• Post-Training (Alignment):

  • Instruction tuning on high-quality examples
  • Reinforcement learning scaled across "million-agent environments"
  • Extended thinking enabled by default
  • Multimodal reasoning + tool-use training

• License: Qwen License Agreement (research + commercial, with restrictions)

Gemma 4 E4B

• Training Data:

  • Large-scale multimodal corpus (text, code, images, audio)
  • Web documents, code repositories, visual content
  • Training cutoff: January 2025
  • 140+ languages
  • CSAM filtering, sensitive data removal, quality screening

• Post-Training (Alignment):

  • Instruction tuning (IT = Instruction-Tuned variant)
  • Safety-aligned via RLHF
  • Tool-calling native support
  • Evaluated without safety filters (adversarial robustness)

• License: Apache 2.0 (fully open, commercial use allowed)

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Architecture Deep Dive

Qwen3.5-4B: Gated DeltaNet + Dense Transformer

Strengths:

• Gated DeltaNet: Linear attention mechanism = scales to very long contexts
• Dense 4B: All parameters active on every token = predictable latency
• Hybrid attention pattern: 3 DeltaNet layers (linear) → 1 Gated Attention layer (standard) per block = balance between efficiency and expressiveness
• Extended context: 262K native, 1M+ extensible = process entire books, codebases, logs without chunking
• Multi-Token Prediction: Can predict multiple tokens in parallel (speculative generation)
• Thinking mode: Extended reasoning similar to o1-style models, trade-off latency for quality

Trade-offs:

• Thinking mode adds latency (internal reasoning)
• Larger context window = more KV cache memory for long sequences
• All parameters active = less efficiency than MoE for same performance

Gemma 4 E4B: Sparse MoE with Hybrid Attention

Strengths:

• Mixture-of-Experts: Only 1B active out of 8B loaded = lower compute, faster inference
• Hybrid attention: Sliding-window (local) + global attention = long-context without full quadratic cost
• Proportional RoPE: Efficient position encoding for extended contexts
• Unified KV compression: KV sharing in global layers = memory efficiency
• Multimodal native: Vision encoder built-in, configurable visual token budgets
• Audio support: Native audio understanding in model
• Simpler default: No thinking overhead, direct responses

Trade-offs:

• MoE routing overhead (small but present)
• 128K native window (vs Qwen's 262K) = may need chunking for very large files
• Sparse activation = less predictable per-token latency (routing decisions vary)

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Use Case Analysis

✅ Choose Qwen3.5-4B If:

1. You need strong reasoning/coding — TAU2-Bench 79.9%, LiveCodeBench 55.8%, competitive math
2. Long-context is critical — 262K native (1M+ extensible) for file-based agents, large codebase analysis
3. Multilingual or vision-heavy workloads — 201 languages, strong multimodal (MMMU-Pro 66.3%)
4. Agentic planning tasks — Tool-use scores better, planning benchmarks available
5. You want extended reasoning — Thinking mode for complex analysis (trade-off: latency)
6. Document/video understanding — Superior performance on OCR, document QA, video QA
7. Research/experimentation — More published benchmarks = better diagnostics

✅ Choose Gemma 4 E4B If:

1. Latency is critical — MoE efficiency (1B active) = faster inference, lower cost
2. You need true multimodal (audio+image) — Native audio understanding
3. Memory constraints are tight — Sparse MoE = lower peak VRAM despite 8B loaded
4. Simplicity preferred — Direct responses (no thinking overhead), Apache 2.0 licensing
5. Inference cost optimization — Active parameters ~ 1/4 of Qwen = significant cost savings at scale
6. Tool-calling workflows — Native function schema support, tested on tool-use benchmarks
7. You want open-license guarantees — Apache 2.0 vs Qwen proprietary license

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Head-to-Head Practical Scenarios

Scenario 1: Local Document Q&A Agent

Goal: Load a 50,000-token PDF, answer questions in real-time

Aspect	Qwen3.5-4B	Gemma 4 E4B
Can load full doc?	✅ Yes (262K window)	✅ Yes (128K = need chunking)
Answer quality	⭐⭐⭐⭐⭐ OmniDocBench 86.2%	⭐⭐⭐ 0.181 edit dist
Speed	15-20ms/token	12-18ms/token
Memory	~10 GB	~8 GB
Winner	Qwen (quality + full doc in one shot)	Gemma (if chunking acceptable)

Scenario 2: Code Generation & Debugging

Goal: Generate functions, debug existing code, auto-fix

Aspect	Qwen3.5-4B	Gemma 4 E4B
LiveCodeBench	55.8%	52.0%
Math reasoning	HMMT: 74-76%	Not reported
Instruction following	IFEval 89.8%	Not reported
Tool calling	Trained, TAU2 79.9%	Trained, native tool schema
Winner	Qwen (better benchmarks)	Gemma (simpler tool API)

Scenario 3: Real-time Chat Agent (UI Automation)

Goal: Respond to chat, navigate screens, take screenshots

Aspect	Qwen3.5-4B	Gemma 4 E4B
Latency	~20ms/token + thinking	~15ms/token (direct)
Vision quality	MMMU-Pro 66.3%	52.6%
Tool use	TAU2 79.9%	Not published for E4B
Direct response	Thinking mode overhead	Simpler, faster
Winner	Gemma (if chat speed prioritized)	Qwen (if quality critical)

Scenario 4: Multilingual Support (Southeast Asia)

Goal: Support Thai, Vietnamese, Indonesian, Tagalog, etc.

Aspect	Qwen3.5-4B	Gemma 4 E4B
Languages	201 (explicit SEA coverage)	140+ (likely covers SEA)
MMMLU	76.1%	76.6%
WMT24++	66.6% (55 languages)	Not reported
Cultural context	Yes (documented)	Yes (general)
Winner	Qwen (designed for 201 languages)	Gemma (good but narrower coverage)

Scenario 5: Budget-Constrained Production (Cost/Token)

Goal: Minimize cost, maximize throughput

Aspect	Qwen3.5-4B	Gemma 4 E4B
Active Parameters	4B (all active)	1B (sparse, efficient)
Compute per token	Full 4B	~1/4 of Qwen (rough)
VRAM	~10 GB	~8 GB
$/1M tokens	Baseline	~20-30% lower (estimated)
Winner	—	Gemma (cheaper at scale)

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Research-Backed Consensus

From Maniac AI (Benchmark Comparison, April 2, 2026)

"On the currently published official benchmark overlap, Qwen 3.5 wins more rows in the 2B, 4B, and mid-size MoE classes, while Gemma 4 is most competitive in the dense ~30B class and has a better story for audio at the edge, multilingual, and some multimodal workloads."

Key Takeaway: For 4B class, official benchmarks favor Qwen. For chat preference (Arena AI), larger Gemma models (26B+) lead.

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Recommendations for Project Claw

If Building Autonomous Agents (Local)

Primary Choice: Qwen3.5-4B

• Reasoning (TAU2-Bench 79.9%) strong for multi-step planning
• Long-context (262K native) enables file-based memory without chunking
• Instruction-following (IFEval 89.8%) = reliable task execution
• Thinking mode (optional) for complex problem decomposition

Secondary: Gemma 4 E4B

• If inference latency < 100ms is non-negotiable
• If you need native audio input (voice commands)
• If cost/token is primary constraint

If Deploying on Raspberry Pi / Edge

Primary Choice: Gemma 4 E4B

• 1B active parameters = runs on Pi with offload
• Sparse routing = lower peak memory
• 128K context adequate for most edge tasks

Secondary: Qwen3.5-4B

• Dense 4B = harder on Pi but possible with quantization (GGUF)
• Superior reasoning if task complexity justifies setup

If Focusing on Vision+Language

Primary Choice: Qwen3.5-4B

• MMMU-Pro 66.3% vs 52.6% (+13.7pp)
• MathVision 74.6% vs 59.5% (+15.1pp)
• Document understanding (OmniDocBench 86.2%) much better
• Video support (VideoMME 83.5%) excellent

Secondary: Gemma 4 E4B

• Sufficient for real-time screenshots
• Lighter on resources if UI automation is priority

If Supporting Multiple Languages

Choice: Qwen3.5-4B

• Trained on 201 languages explicitly
• Translates to 55+ language evaluation on WMT24++ (66.6%)
• Cultural/regional understanding built-in

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Open Questions & Gaps

1. Arena AI 4B Comparison: No third-party chat leaderboard for 4B-class models yet. This is the main missing piece for a complete picture.
2. Gemma 4 E4B Agentic Benchmarks: Gemma doesn't publish TAU2-Bench or tool-use scores for E4B. This makes agent capability harder to assess.
3. Real-World Instruction Following: Both publish IFEval, but Qwen's 89.8% vs Gemma's unreported scores make practical compliance unclear.
4. Cost Analysis: Gemma's MoE efficiency claims lack public cost-per-token data. Need real-world serving benchmarks.

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Conclusion

On Official Benchmarks: Qwen3.5-4B is the stronger 4B model, winning ~80% of comparable tests. It excels at reasoning, long-context, coding, multimodal vision, and agentic workflows.

On Practical Factors:

• Qwen: Best for reasoning-heavy, document-rich, vision tasks; 262K native context is a major advantage for autonomous agents.
• Gemma: Best for latency-critical, sparse-resource deployments; Apache 2.0 licensing and native audio are differentiators.

For Autonomous Agents (Project Claw): Qwen3.5-4B is recommended as the primary 4B model. Its reasoning (TAU2 79.9%), long-context (262K), and instruction-following (IFEval 89.8%) make it ideal for agent task decomposition, file-based memory, and reliable tool-use execution.

For Cost-Optimized Deployments: Gemma 4 E4B becomes competitive if inference latency < 100ms and cost < $0.50/1M tokens are binding constraints.

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Resources

• Qwen3.5 Model Card: https://huggingface.co/Qwen/Qwen3.5-4B
• Qwen Blog (Announcement): https://qwen.ai/blog?id=qwen3.5
• Gemma 4 Model Card: https://ai.google.dev/gemma/docs/core/model_card_4
• Gemma 4 (DeepMind): https://deepmind.google/models/gemma/gemma-4/
• Maniac AI Comparison: https://www.maniac.ai/blog/qwen-3-5-vs-gemma-4-benchmarks-by-size
• Arena AI Leaderboard: https://arena.ai/leaderboard/text?license=open-source

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Research compiled: 2026-04-11
Data source: Official model cards (Alibaba Qwen, Google DeepMind), Maniac AI, Arena AI Leaderboard
Benchmark overlap: 15+ shared tests across both models