Gemma 4 E4B vs Gemma 4 E2B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Gemma 4 E4B by Google and Gemma 4 E2B by Google 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, both models provide an identical memory horizon with 131K tokens. In latency-sensitive production environments, Gemma 4 E2B 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.
किसी भी मॉडल को बदलें और तुलना करें
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Gemma 4 E4B
ओपन-वेट्स मॉडलGoogle द्वारा विकसित Gemma 4 E4B एक उन्नत AI मॉडल है, जिसमें 131K टोकन का कॉन्टेक्स्ट विंडो और $0/1M इनपुट तथा $0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
Gemma 4 E2B
ओपन-वेट्स मॉडलGoogle द्वारा विकसित Gemma 4 E2B एक उन्नत AI मॉडल है, जिसमें 131K टोकन का कॉन्टेक्स्ट विंडो और $0/1M इनपुट तथा $0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
एक नज़र में मुख्य अंतर
- Both models cost the same under this workload — choose on capability, not price.
- Gemma 4 E2B is faster at 260 tok/s vs 190 tok/s.
| Feature | Gemma 4 E4B | Gemma 4 E2B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 65 | 55 |
Provider | ||
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 | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 16K | 8K |
Throughput Speed Tokens generated per second | 190 tok/s | 260 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
किस क्षेत्र में कौन आगे है?
At-a-glance decision frameworkEvaluations ongoing
सत्यापित बेंचमार्क स्कोर तुलना
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.
लेटेंसी और रिस्पॉन्स समय
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
रियल-टाइम टोकन लागत सिम्युलेटर
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemma 4 E4B · Strengths & Key Advantages
- Google की उन्नत Edge-optimized dense open transformer आर्किटेक्चर और 131K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0/1M इनपुट, $0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Gemma 4 E2B · Strengths & Key Advantages
- Google की उन्नत Ultra-compact edge transformer आर्किटेक्चर और 131K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0/1M इनपुट, $0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 4 E4B | Gemma 4 E2B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 4K | 2K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 1K |
तकनीकी विशिष्टताएँ (Specs)
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 4 E4B | Gemma 4 E2B |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-e4b | models/gemma-4-e2b |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | January 2026 | January 2026 |
| Architecture | Edge-optimized dense open transformer | Ultra-compact edge transformer |
| Parameters | E4B (4B) | E2B (2B) |
| 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) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| 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) · Vision (~258–765 tokens per image tile) |
| 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) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Google DeepMind Frontier Safety Framework Verified |
| 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, vision |
| Output Modalities | text | text |
Gemma 4 E4B vs Gemma 4 E2B: त्वरित प्रश्नोत्तर (FAQ)
Which is more intelligent, Gemma 4 E4B or Gemma 4 E2B?
Gemma 4 E4B is more intelligent. It scores 65 compared with 55 on the composite Intelligence Index.
Which model is faster?
Gemma 4 E2B is faster, generating 260 tokens per second compared with 190 tokens per second.
Which model has lower latency?
Gemma 4 E2B has lower latency, with a time to first token of 0.18s compared with 0.22s.
निष्कर्ष
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 Gemma 4 E2B if your workflow prioritizes Smartphones, IoT microcontrollers, and ultra-fast local keyword/entity extractors. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
अक्सर पूछे जाने वाले प्रश्न (FAQ)
How does Gemma 4 E4B compare to Gemma 4 E2B in API token pricing and cost per token?+
Both Gemma 4 E4B and Gemma 4 E2B 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 Gemma 4 E2B?+
Both Gemma 4 E4B and Gemma 4 E2B offer an identical context window of 131K tokens (~98k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Gemma 4 E4B or Gemma 4 E2B?+
Gemma 4 E4B scores at the frontier of coding benchmarks, while Gemma 4 E2B 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 Gemma 4 E2B?+
Gemma 4 E4B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemma 4 E2B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemma 4 E4B and Gemma 4 E2B?+
Gemma 4 E4B generates approximately 190 tokens per second, while Gemma 4 E2B delivers 260 tokens per second. In latency-sensitive production environments, Gemma 4 E2B 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 Gemma 4 E2B 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 E2B, 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.
स्रोत: GetInfoUs Independent AI Benchmarks & Official API Specs