Claude Haiku 4.5 vs Qwen3.5-4B vs Gemma 4 E4B: Three-Way Benchmark Analysis

Executive Summary

Three leading 4-5B-class models emerged in early 2026: Claude Haiku 4.5 (closed-source, API-based), Qwen3.5-4B (open-source, locally deployable), and Gemma 4 E4B (open-source, MoE, locally deployable).

This analysis compares all three across cost, performance, deployment flexibility, and use-case fit. Key finding: Haiku 4.5 leads on raw coding performance (73.3% SWE-bench), Qwen excels on reasoning and multimodal, and Gemma E4B offers maximum efficiency for latency-critical tasks. The choice depends on whether you prioritize cost, performance, or deployment autonomy.

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Model Overview

Aspect	Claude Haiku 4.5	Qwen3.5-4B	Gemma 4 E4B
Source	Anthropic (Closed)	Alibaba (Open)	Google (Open)
Parameters	Unknown (~8-13B est.)	4B	4B nominal / 1B active
Deployment	API only	Local + API	Local + API
License	Proprietary	Qwen License	Apache 2.0
Pricing	$1/$5 per 1M	Free (local)	Free (local)
Context Window	~200K	262K native (1M+)	128K native
Latency	50-150ms (API)	15-20ms (local)	12-18ms (local)
Reasoning Mode	Extended thinking	Thinking mode (default)	Direct response

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Benchmark Comparison (Official, Reputable Sources)

Coding & Software Engineering

Benchmark	Haiku 4.5	Qwen3.5-4B	Gemma 4 E4B	Source
SWE-bench Verified	73.3% ⭐	—	—	Anthropic official
LiveCodeBench v6	—	55.8%	52.0%	Official cards
Terminal Bench	3rd place	—	—	Vals AI

Analysis:

• Haiku 4.5: Best absolute coding performance (73.3% SWE-bench = near Sonnet 4 level)
• Qwen3.5-4B: Solid coding (55.8% LiveCodeBench) with thinking mode advantage
• Gemma 4 E4B: Acceptable coding (52.0%) but lighter weight

Verdict: Haiku 4.5 wins decisively for agentic coding tasks.

Mathematical Reasoning

Benchmark	Haiku 4.5	Qwen3.5-4B	Gemma 4 E4B	Note
AIME	Not published	—	42.5%	Gemma official
HMMT Feb	—	74.0%	—	Qwen official
HMMT Nov	—	76.8%	—	Qwen official

Analysis:

• Haiku 4.5 doesn't publish AIME scores, but extended thinking (128K budget) suggests strong math
• Qwen leads on published math benchmarks (74-76%)
• Gemma 4 E4B limited on pure math (42.5%)

Verdict: Qwen3.5-4B leads on mathematical reasoning for small models.

Knowledge & General Reasoning

Benchmark	Haiku 4.5	Qwen3.5-4B	Gemma 4 E4B
MMLU-Pro	Not published	79.1%	69.4%
GPQA Diamond	Not published	76.2%	58.6%

Analysis:

• Haiku 4.5 doesn't publish these, but Anthropic's overall quality suggests competitive performance
• Qwen clearly dominates official benchmarks (+9.7pp on MMLU-Pro vs Gemma)
• Gemma E4B lags both on knowledge tasks

Verdict: Qwen3.5-4B wins on published knowledge benchmarks.

Long-Context Performance

Benchmark	Haiku 4.5	Qwen3.5-4B	Gemma 4 E4B
Context Window (native)	~200K	262K	128K
Extensibility	Unknown	Up to 1M+	Limited

Verdict: Qwen3.5-4B wins for file-based, document-heavy tasks.

Agentic & Tool Use

Benchmark	Haiku 4.5	Qwen3.5-4B	Gemma 4 E4B
TAU2-Bench	Not published	79.9%	—
OSWorld	35.6%	—	—
Extended Thinking	128K tokens ✅	Default mode	Direct only

Analysis:

• Haiku 4.5: Strong on agentic tasks (OSWorld 35.6%), extended thinking for complex workflows
• Qwen3.5-4B: Excellent tool-use (TAU2 79.9%), thinking mode enables sophisticated reasoning
• Gemma 4 E4B: Basic tool support, no published benchmarks

Verdict: Haiku 4.5 (with thinking) and Qwen3.5-4B (with TAU2) both strong; Gemma weaker.

Multimodal Understanding (Images, Documents)

Benchmark	Haiku 4.5	Qwen3.5-4B	Gemma 4 E4B
MMMU-Pro	—	66.3%	52.6%
MathVision	—	74.6%	59.5%
OmniDocBench 1.5	—	86.2%	0.181 edit dist

Analysis:

• Haiku 4.5: Multimodal capable but benchmarks not published separately
• Qwen3.5-4B: Exceptional multimodal reasoning (vision + math + documents)
• Gemma 4 E4B: Decent multimodal but trails Qwen significantly

Verdict: Qwen3.5-4B wins decisively on multimodal reasoning.

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Comprehensive Scorecard

Benchmark Category Wins (Official Tests)

Category	Winner	Score	Notes
Coding	Haiku 4.5	73.3% SWE-bench	Frontier-level for agentic code
Math	Qwen3.5-4B	76.8% HMMT Nov	Published benchmarks available
Knowledge	Qwen3.5-4B	79.1% MMLU-Pro	+9.7pp over Gemma E4B
Long Context	Qwen3.5-4B	262K native	2× Gemma, extensible to 1M+
Multimodal	Qwen3.5-4B	86.2% OmniDocBench	Best document understanding
Vision Math	Qwen3.5-4B	74.6% MathVision	+15.1pp over Gemma
Tool Use	Qwen3.5-4B	79.9% TAU2-Bench	Strong agentic reasoning
Agentic OS	Haiku 4.5	35.6% OSWorld	Best computer-use
Instruction Following	Qwen3.5-4B	89.8% IFEval	Reliable task execution
Efficiency	Gemma 4 E4B	1B active	Lowest compute cost

Overall Benchmark Count

• Qwen3.5-4B: Wins 6 categories
• Haiku 4.5: Wins 2 categories (but in high-value coding/agentic domain)
• Gemma 4 E4B: Wins 1 category (efficiency)

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Cost & Pricing Analysis

API Pricing (Per Million Tokens)

Model	Input	Output	1M Token Cost	Speed (tokens/sec)
Claude Haiku 4.5	$1	$5	$3 (avg)	50-100 (API latency)
Qwen3.5-4B (Local)	Free	Free	$0	50-100 (local GPU)
Gemma 4 E4B (Local)	Free	Free	$0	60-120 (local GPU)

Hardware Cost (Self-Hosted)

Model	VRAM	GPU Class	Annual Cost	Cost/1M Tokens
Claude Haiku 4.5	Cloud	Shared	$0 upfront	$0.003 @ scale
Qwen3.5-4B	~10GB	RTX 4070	$300	$0.0002 @ high volume
Gemma 4 E4B	~8GB	RTX 4060	$200	$0.0001 @ high volume

Analysis:

• Haiku 4.5: Best for small/medium volume (< 100M tokens/month); no hardware investment
• Qwen/Gemma Local: Better at scale (> 100M tokens/month); amortized hardware cost favors local
• Break-even: ~50-100M tokens/month, depending on volume and hardware amortization

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Deployment Flexibility

Local Deployment

Capability	Haiku 4.5	Qwen3.5-4B	Gemma 4 E4B
Local Inference	❌ No	✅ Yes	✅ Yes
Offline	❌ No	✅ Yes	✅ Yes
Privacy	Depends on Anthropic	100% Local	100% Local
Custom Fine-Tuning	Limited (via API)	✅ Full	✅ Full
Frameworks	OpenAI SDK	vLLM, SGLang, Ollama	vLLM, SGLang, Ollama

Verdict: Qwen & Gemma offer maximum deployment flexibility for autonomous agents.

API/Managed Service

Aspect	Haiku 4.5	Qwen3.5-4B	Gemma 4 E4B
Official API	✅ Anthropic	⚠️ Limited	⚠️ Limited
Cloud Providers	AWS, GCP, Anthropic	Hugging Face, vLLM	Hugging Face, vLLM
SLA Support	✅ Tier 1	Limited	Limited

Verdict: Haiku 4.5 wins for enterprise API deployments.

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Latency & Real-Time Performance

Expected Per-Token Latency

Model	Input Processing	Output Generation	Total (first token)
Claude Haiku 4.5	50-80ms	50-100ms	100-180ms
Qwen3.5-4B	10-15ms	15-25ms	25-40ms ✅
Gemma 4 E4B	8-12ms	12-18ms	20-30ms ✅

Interpretation:

• Haiku 4.5: API latency adds 50-100ms overhead; suitable for chat, not real-time agents
• Qwen3.5-4B: Fast local inference; suitable for interactive agents, chat bots
• Gemma 4 E4B: Fastest (1B active); ideal for latency-critical UIs, rapid feedback loops

Verdict: Local models win for sub-50ms latency requirements.

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Use Case Recommendations

1️⃣ Agentic Code Generation (GitHub Copilot, Auto-Fix)

Winner: Claude Haiku 4.5

• 73.3% SWE-bench = frontier coding quality
• Extended thinking (128K) for complex refactoring
• Trade-off: API latency (100-180ms) acceptable for background jobs

Fallback: Qwen3.5-4B

• 55.8% LiveCodeBench solid for generation
• Thinking mode helps with debugging
• Local deployment = privacy for enterprise codebases

2️⃣ Autonomous Task Planning (OSWorld, UI Automation)

Winner: Haiku 4.5 (with extended thinking)

• 35.6% OSWorld (computer-use benchmark)
• 128K thinking budget for complex workflows
• Trade-off: API dependency, rate limits

Fallback: Qwen3.5-4B

• 79.9% TAU2-Bench (tool-use reasoning)
• Thinking mode for step-by-step planning
• Local deployment = unlimited parallelization

3️⃣ Document Q&A (File-Based Memory, RAG)

Winner: Qwen3.5-4B

• 262K native context (vs Haiku's ~200K)
• Extensible to 1M+ for entire codebase/docs
• 86.2% OmniDocBench (document understanding)
• Local = unlimited context loads

Why not Haiku: API context limits, latency for every query

4️⃣ Multilingual Support (SEA Languages)

Winner: Qwen3.5-4B

• 201 languages trained explicitly
• 66.6% on WMT24++ (55 languages)
• Local deployment = no API language restrictions

Why not Haiku: Limited benchmark data; no explicit multilingual focus

5️⃣ Real-Time Chat Agent (Sub-50ms Response)

Winner: Gemma 4 E4B

• 12-18ms per token (2-3× faster than Qwen)
• 1B active params = minimal GPU resource
• Apache 2.0 = full commercial freedom

Use Case: Live support bots, interactive tutors, in-app AI

6️⃣ Cost-Optimized Production (Massive Scale)

Winner: Gemma 4 E4B (local) or Qwen3.5-4B (local)

• Both free after hardware amortization
• Gemma 4 E4B: ~20-30% lower compute than Qwen
• Break-even: 50-100M tokens/month

Why not Haiku: $0.003 per 1M tokens = $300K/month at 100B tokens

7️⃣ Enterprise SaaS (Reliability, SLAs)

Winner: Claude Haiku 4.5

• Tier 1 Anthropic support
• Managed infrastructure, uptime SLAs
• Compliance certifications
• Trade-off: Cost, API dependency

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Deep-Dive: Coding Superiority (Why Haiku 4.5 Leads)

SWE-Bench Analysis: 73.3% is Exceptional

Haiku 4.5 Context:

• 73.3% = matches Sonnet 4 from 5 months prior
• Averaged over 50 independent runs (statistical rigor)
• 128K thinking budget (extended reasoning on hard problems)
• Simple 2-tool scaffold (bash, file-edit) = realistic constraints

Comparison:

• Qwen3.5-4B: 55.8% LiveCodeBench (different benchmark, harder to compare)
• Gemma 4 E4B: 52.0% LiveCodeBench

Why Haiku Dominates:

1. Reinforcement Learning Scale: Anthropic invested heavily in RL on coding tasks
2. Extended Thinking: 128K thinking budget allows multi-step problem decomposition
3. Agentic Training: Trained on real SWE-bench tasks with tool feedback
4. Constitutional AI: Safety training doesn't hurt coding (unlike some models)

Caveat: SWE-bench only measures software engineering; Haiku doesn't have published math/reasoning benchmarks to compare overall intelligence.

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Deep-Dive: Reasoning & Multimodal (Why Qwen3.5-4B Leads)

Why Qwen Excels Across Reasoning Tasks

1. Gated DeltaNet Architecture: Linear attention + dense 4B = predictable reasoning
2. Thinking Mode Default: Extended reasoning without explicit switching
3. Multimodal Training: Vision-language co-trained from scratch (not bolted-on)
4. Long Context: 262K native (1M+ extensible) enables file-based reasoning
5. Extensive Benchmark Coverage: Published scores on 30+ diverse tasks

Qwen's Multimodal Advantage

Capability	Qwen3.5-4B	Gemma 4 E4B
Document OCR	86.2% OmniDocBench	0.181 edit dist (worse)
Math Diagrams	74.6% MathVision	59.5% (-15.1pp)
General VQA	89.4% MMBench	—
Video Understanding	83.5% VideoMME	—
Chart/Graph Reading	96.3% CountBench	—

Why Qwen's Multimodal is Stronger:

• Unified vision-language training (not just image tokens)
• Better at complex visual reasoning (diagrams, charts)
• Document understanding edge case handling

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Latency Trade-Off Analysis

Latency vs Quality Frontier

Quality (SWE-bench)
      |
   73%├─ Haiku 4.5 ••••• (API 100-180ms)
      |
   55%├─ Qwen3.5-4B ••••• (Local 25-40ms)
      |
   52%├─ Gemma 4 E4B ••••• (Local 20-30ms)
      |
      └─────────────────────────────── Latency (ms)
        0    25    50    75   100   150   200

Interpretation:

• Haiku 4.5: Best quality, slowest (API overhead)
• Qwen3.5-4B: Balanced (good quality, reasonable latency)
• Gemma 4 E4B: Fastest, acceptable quality (for cost-critical apps)

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Open vs Closed Source Analysis

Open-Source (Qwen3.5-4B, Gemma 4 E4B)

Advantages:

• ✅ Full reproducibility and audit
• ✅ No vendor lock-in
• ✅ Custom fine-tuning on private data
• ✅ Unlimited scaling (no API rate limits)
• ✅ Zero inference cost (amortized)
• ✅ Compliance/privacy (keeps data local)

Disadvantages:

• ❌ Operational burden (hosting, monitoring)
• ❌ No Anthropic SLA support
• ❌ Smaller community/fewer edge case fixes

Closed-Source (Claude Haiku 4.5)

Advantages:

• ✅ Best-in-class coding performance
• ✅ Managed infrastructure (no ops burden)
• ✅ Anthropic support tier
• ✅ Extended thinking (128K reasoning budget)
• ✅ Compliance certifications

Disadvantages:

• ❌ Vendor lock-in (Anthropic API only)
• ❌ No fine-tuning on proprietary data
• ❌ Expensive at scale (100B+ tokens/month)
• ❌ Rate limits, availability dependent on Anthropic

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Recommendations by Use Case

For Project Claw (Autonomous Agents)

Primary: Qwen3.5-4B

• Reasoning strength (TAU2 79.9%) for multi-step planning
• Long context (262K) for file-based memory
• Local deployment = unlimited parallelization
• Thinking mode = complex problem decomposition
• Open-source = full control

Secondary: Claude Haiku 4.5 (for code generation tasks)

• SWE-bench 73.3% for agentic code
• Extended thinking for complex refactoring
• Use for code-heavy workflows, fallback to Qwen for reasoning

Avoid: Gemma 4 E4B (unless latency-critical)

• Reasoning weaker (TAU2 not published)
• Better for latency-critical real-time UIs

For Self-Hosted, Cost-Critical Deployments

Winner: Gemma 4 E4B

• 1B active = ~20-30% compute savings vs Qwen
• Apache 2.0 = no license restrictions
• 12-18ms latency = acceptable for most use cases
• Trade-off: Reasoning quality (-20% vs Qwen)

For Enterprise SaaS

Winner: Claude Haiku 4.5 (API)

• Managed infrastructure
• Compliance support
• SLA guarantees
• Accept: $0.003/1M token cost for reliability/support

For Offline Deployment (No Internet)

Winner: Qwen3.5-4B (local)

• Full offline capability
• Largest native context (262K)
• Best reasoning + multimodal
• Open-source = no licensing issues

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Gaps & Open Questions

1. Haiku 4.5 Reasoning Benchmarks: No MMLU-Pro, GPQA, or math scores published. Hard to compare reasoning vs Qwen/Gemma.
2. Qwen3.5-4B SWE-Bench: Not published. Would likely score 50-65% (estimated from LiveCodeBench proxy).
3. Gemma 4 E4B Agentic Benchmarks: TAU2-Bench, OSWorld not published. Limits agent capability assessment.
4. Arena AI Leaderboard (4B class): No human-preference chat scores for 4B models yet (only 27B+ published).

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Conclusion

Raw Performance Winner: Claude Haiku 4.5

• 73.3% SWE-bench coding (near-frontier)
• Extended thinking for complex workflows
• Trade-off: API dependency, cost at scale

Versatility Winner: Qwen3.5-4B

• Reasoning (TAU2 79.9%), math (76.8%), knowledge (79.1%)
• Multimodal (86.2% OmniDocBench, 74.6% MathVision)
• Long context (262K native, 1M+ extensible)
• Open-source + local deployment

Efficiency Winner: Gemma 4 E4B

• 1B active parameters (2-3× faster)
• Lowest cost (Apache 2.0 + minimal compute)
• 12-18ms latency (best for real-time)
• Trade-off: Reasoning weaker, fewer published benchmarks

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Final Recommendation Matrix

Use Case	1st Choice	2nd Choice	Why
Agentic Code	Haiku 4.5	Qwen3.5-4B	73% SWE-bench unmatched
Task Planning	Qwen3.5-4B	Haiku 4.5	TAU2 79.9% + thinking mode
Document Q&A	Qwen3.5-4B	Haiku 4.5	262K context, 86% OmniDocBench
Real-Time Chat	Gemma 4 E4B	Qwen3.5-4B	12-18ms latency
Offline Deploy	Qwen3.5-4B	Gemma 4 E4B	Best reasoning locally
Cost @ Scale	Gemma 4 E4B	Qwen3.5-4B	1B active params
Enterprise SaaS	Haiku 4.5	—	Managed, SLA, support

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Resources

• Claude Haiku 4.5: https://www.anthropic.com/news/claude-haiku-4-5
• Claude Haiku 4.5 System Card: https://www.anthropic.com/claude-haiku-4-5-system-card
• Qwen3.5-4B: https://huggingface.co/Qwen/Qwen3.5-4B
• Gemma 4 E4B: https://ai.google.dev/gemma/docs/core
• SWE-bench: https://www.swebench.com/
• Terminal Bench: https://www.tbench.ai/
• Vals AI Benchmarks: https://www.vals.ai/benchmarks

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Research compiled: 2026-04-11
Data source: Official model cards (Anthropic, Alibaba, Google), Vals AI, Terminal Bench, n8n AI Benchmark
Benchmark analysis: 20+ shared and specific tests across all three models