Gemma 4 E2B vs Gemma 4 31B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemma 4 E2B by Google and Gemma 4 31B 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, Gemma 4 31B provides an expanded memory horizon with 262K 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.
Switch & Compare Any Models
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Gemma 4 E2B
On-Device 2BGoogle's most lightweight 2B open model in the Gemma 4 lineup, designed for resource-constrained edge chips.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
Gemma 4 31B
Open ReasoningLargest dense open reasoning model in the Gemma 4 series featuring 262K context and native chain-of-thought.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
68.5%
Key Differences at a Glance
- Both models cost the same under this workload — choose on capability, not price.
- Gemma 4 31B has a larger context window (262K vs 131K tokens).
- Gemma 4 E2B is faster at 260 tok/s vs 80 tok/s.
| Feature | Gemma 4 E2B | Gemma 4 31B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 81 |
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 | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 8K | 33K |
Throughput Speed Tokens generated per second | 260 tok/s | 80 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 68.5% |
GPQA Diamond Graduate-level reasoning | N/A | 77% |
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.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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 E2B · Strengths & Key Advantages
- Ultra-low memory consumption (under 2.5GB)
- Blazing fast 260 tps on consumer laptops
- Open weights
Considerations & Trade-offs
- Basic reasoning only
- Struggles with convoluted multi-step puzzles
Gemma 4 31B · Strengths & Key Advantages
- Frontier-grade reasoning in an open-weight 31B package
- Expansive 262K context window
- Competitive coding and math benchmark scores
Considerations & Trade-offs
- Requires 32GB+ VRAM or 4-bit quantisation to run locally
- Slower generation speed when chain-of-thought is active
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 4 E2B | Gemma 4 31B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 2K | 12K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 6K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 4 E2B | Gemma 4 31B |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-e2b | models/gemma-4-31b |
| 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 | Ultra-compact edge transformer | Dense open reasoning transformer with native thinking |
| Parameters | E2B (2B) | 31B |
| 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) · 4 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 67 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 E2B vs Gemma 4 31B: Quick Answers
Which is more intelligent, Gemma 4 E2B or Gemma 4 31B?
Gemma 4 31B is more intelligent. It scores 81 compared with 55 on the composite Intelligence Index.
Which model is faster?
Gemma 4 E2B is faster, generating 260 tokens per second compared with 80 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 0.48s.
Which model has a larger context window?
Gemma 4 31B has a larger context window, supporting 262.1K tokens compared with 131.1K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Gemma 4 E2B if your priority is Smartphones, IoT microcontrollers, and ultra-fast local keyword/entity extractors. Its performance in demanding tasks justifies its operational token cost. Choose Gemma 4 31B if your workflow prioritizes Private enterprise reasoning servers, local software development, and deep research. 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 E2B compare to Gemma 4 31B in API token pricing and cost per token?+
Both Gemma 4 E2B and Gemma 4 31B 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 E2B or Gemma 4 31B?+
Gemma 4 E2B supports a context window of 131K tokens (~98k words), while Gemma 4 31B supports 262K tokens (~197k words). Therefore, Gemma 4 31B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemma 4 E2B or Gemma 4 31B?+
Gemma 4 E2B scores at the frontier of coding benchmarks, while Gemma 4 31B scores 68.5%. For complex autonomous debugging and agentic engineering, Gemma 4 31B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemma 4 E2B and Gemma 4 31B?+
Gemma 4 E2B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemma 4 31B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemma 4 E2B and Gemma 4 31B?+
Gemma 4 E2B generates approximately 260 tokens per second, while Gemma 4 31B delivers 80 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 E2B and Gemma 4 31B 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 E2B. 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.
Related reading
- DeepSeek V4 vs Gemma 4 E2B: Benchmarks, Pricing & Speed ComparisonCompare
- Gemma 4 31B vs Gemma 4 26B A4B: Benchmarks, Pricing & Speed ComparisonCompare
- Gemma 4 31B vs Llama 3.3 70B: Benchmarks, Pricing & Speed ComparisonCompare
- Gemma 4 31B vs Mistral Small 4: Benchmarks, Pricing & Speed ComparisonCompare
- Gemma 4 E4B vs Gemma 4 E2B: Benchmarks, Pricing & Speed ComparisonCompare
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed ComparisonCompare
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