Gemma 3n vs Gemma 3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Gemma 3n by Google and Gemma 3 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 3 provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Gemma 3n 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 3n
On-Device OpenCompact on-device open model optimized for phones, edge devices, and offline voice/vision assistants.
$0.00 / 1M
$0.00 / 1M
32K tokens
N/A
Gemma 3
Multimodal OpenMultimodal open model family with 128K context window, native vision understanding, and multi-lingual support.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
Key Differences at a Glance
- Both models cost the same under this workload — choose on capability, not price.
- Gemma 3 has a larger context window (131K vs 32K tokens).
- Gemma 3n is faster at 150 tok/s vs 105 tok/s.
| Feature | Gemma 3n | Gemma 3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 68 |
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 | 32K tokens~48 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 4K | 16K |
Throughput Speed Tokens generated per second | 150 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | N/A | 61% |
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.
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.
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 3n · Strengths & Key Advantages
- Runs on modern smartphones and Apple Silicon with minimal power
- Handles live audio and vision inputs locally
- Open weights
Considerations & Trade-offs
- Smaller 32K context horizon
- Lower reasoning depth on nuanced programming
Gemma 3 · Strengths & Key Advantages
- Native vision ingestion in an open weight model
- Spacious 128K context window
- Strong performance across 100+ languages
Considerations & Trade-offs
- Requires 24GB+ VRAM for the 27B variant
- Lacks extended chain-of-thought mode
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 3n | Gemma 3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 3K | 5K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 3n | Gemma 3 |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-3n | models/gemma-3 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-06 | 2025-03 |
| Knowledge Cutoff | March 2025 | December 2024 |
| Architecture | Edge-optimized multimodal transformer with audio support | Native multimodal open transformer with expanded context |
| Parameters | 2B / 5B | 1B / 4B / 12B / 27B |
| 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) · Audio (~25–32 tokens/sec) | 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) · 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, audio | text, vision |
| Output Modalities | text | text |
Gemma 3n vs Gemma 3: Quick Answers
Which is more intelligent, Gemma 3n or Gemma 3?
Gemma 3 is more intelligent. It scores 68 compared with 52 on the composite Intelligence Index.
Which model is faster?
Gemma 3n is faster, generating 150 tokens per second compared with 105 tokens per second.
Which model has lower latency?
Gemma 3n has lower latency, with a time to first token of 0.22s compared with 0.32s.
Which model has a larger context window?
Gemma 3 has a larger context window, supporting 131.1K tokens compared with 32K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Gemma 3n if your priority is Embedded hardware, mobile edge intelligence, and offline voice interfaces. Its performance in demanding tasks justifies its operational token cost. Choose Gemma 3 if your workflow prioritizes Self-hosted multimodal applications, local image QA, and long document parsing. 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 3n compare to Gemma 3 in API token pricing and cost per token?+
Both Gemma 3n and Gemma 3 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 3n or Gemma 3?+
Gemma 3n supports a context window of 32K tokens (~24k words), while Gemma 3 supports 131K tokens (~98k words). Therefore, Gemma 3 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemma 3n or Gemma 3?+
Gemma 3n scores at the frontier of coding benchmarks, while Gemma 3 scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemma 3n provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemma 3n and Gemma 3?+
Gemma 3n is optimized for low-latency direct generation. In contrast, Gemma 3 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Gemma 3n and Gemma 3?+
Gemma 3n generates approximately 150 tokens per second, while Gemma 3 delivers 105 tokens per second. In latency-sensitive production environments, Gemma 3n provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemma 3n and Gemma 3 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 3n, and route deep architectural or complex reasoning queries to Gemma 3n. 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