Gemma 4 E4B vs Hy3 preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Gemma 4 E4B by Google and Hy3 preview by Tencent represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Hy3 preview provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Gemma 4 E4B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Gemma 4 E4B
On-Device 4BCompact 4B open model engineered for fast on-device inference, coding subagents, and responsive apps.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
Hy3 preview
PreviewEarly preview release of the Hy3 reasoning architecture allowing developers to test next-generation instruction following and tool loops.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Key Differences at a Glance
- Both models cost the same under this workload — choose on capability, not price.
- Hy3 preview has a larger context window (262K vs 131K tokens).
- Gemma 4 E4B is faster at 190 tok/s vs 88 tok/s.
| Feature | Gemma 4 E4B | Hy3 preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 65 | 79 |
Provider | Tencent | |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 8K |
Throughput Speed Tokens generated per second | 190 tok/s | 88 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
Official evaluation metrics verified by independent third-party research labs.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemma 4 E4B · Strengths & Key Advantages
- Lightweight memory footprint (under 5GB quantized)
- Runs smoothly on laptops and mobile devices
- 128K context window
Considerations & Trade-offs
- Narrower world knowledge breadth than 31B
- Simplified chain-of-thought depth
Hy3 preview · Strengths & Key Advantages
- Early access to Tencent updated MoE reasoning architecture
- Free open-weights testing for enterprise evaluation
- Good baseline performance across Chinese and English tasks
Considerations & Trade-offs
- Preview build with occasional experimental edge-case variations
- Superseded by the final production Hy3 release
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 4 E4B | Hy3 preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 4K | 15K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 3K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 4 E4B | Hy3 preview |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-e4b | hy3-preview |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | January 2026 | February 2026 |
| Architecture | Edge-optimized dense open transformer | Preview Open Reasoning MoE Transformer |
| Parameters | E4B (4B) | Confidential / 140B MoE |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 9 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 87 GB VRAM (4-bit Quantized) · 301 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Gemma 4 E4B vs Hy3 preview: Quick Answers
Which is more intelligent, Gemma 4 E4B or Hy3 preview?
Hy3 preview is more intelligent. It scores 79 compared with 65 on the composite Intelligence Index.
Which model is faster?
Gemma 4 E4B is faster, generating 190 tokens per second compared with 88 tokens per second.
Which model has lower latency?
Gemma 4 E4B has lower latency, with a time to first token of 0.22s compared with 0.44s.
Which model has a larger context window?
Hy3 preview has a larger context window, supporting 262.1K tokens compared with 131.1K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Gemma 4 E4B if your priority is Edge robotics, laptop coding assistants, and private personal assistants. Its performance in demanding tasks justifies its operational token cost. Choose Hy3 preview if your workflow prioritizes Early benchmarking, architectural evaluation, and experimental agent testing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Gemma 4 E4B compare to Hy3 preview in API token pricing and cost per token?+
Both Gemma 4 E4B and Hy3 preview are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Gemma 4 E4B or Hy3 preview?+
Gemma 4 E4B supports a context window of 131K tokens (~98k words), while Hy3 preview supports 262K tokens (~197k words). Therefore, Hy3 preview can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemma 4 E4B or Hy3 preview?+
Gemma 4 E4B scores at the frontier of coding benchmarks, while Hy3 preview scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemma 4 E4B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemma 4 E4B and Hy3 preview?+
Gemma 4 E4B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Hy3 preview provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemma 4 E4B and Hy3 preview?+
Gemma 4 E4B generates approximately 190 tokens per second, while Hy3 preview delivers 88 tokens per second. In latency-sensitive production environments, Gemma 4 E4B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemma 4 E4B and Hy3 preview to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Gemma 4 E4B, and route deep architectural or complex reasoning queries to Gemma 4 E4B. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs