Gemma 3 vs Llama 4 Scout
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemma 3 by Google and Llama 4 Scout by Meta 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, Llama 4 Scout provides an expanded memory horizon with 3,500K tokens. In latency-sensitive production environments, Gemma 3 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 3
オープンウェイトモデルGoogle が提供する Gemma 3 は、最大 131K トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
Llama 4 Scout
オープンウェイトモデルMeta が提供する Llama 4 Scout は、最大 3.5M トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
3.5M tokens
N/A
ひと目でわかる主な違い
- Both models cost the same under this workload — choose on capability, not price.
- Llama 4 Scout has a larger context window (4M vs 131K tokens).
- Gemma 3 is faster at 105 tok/s vs 95 tok/s.
| Feature | Gemma 3 | Llama 4 Scout |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 68 | 76 |
Provider | Meta | |
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 | 3.5M tokens~5,250 A4 pages of text |
Max Output Tokens Per single request | 16K | 33K |
Throughput Speed Tokens generated per second | 105 tok/s | 95 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 61% | N/A |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemma 3 · Strengths & Key Advantages
- Google の高度な Native multimodal open transformer with expanded context アーキテクチャと 131K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Llama 4 Scout · Strengths & Key Advantages
- Meta の高度な Extreme-context sparse MoE multimodal transformer (17B active / 109B total) アーキテクチャと 3.5M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 3 | Llama 4 Scout |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 5K | 10K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 3 | Llama 4 Scout |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-3 | meta-llama/Llama-4-Scout |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-03 | 2025-04 |
| Knowledge Cutoff | December 2024 | January 2025 |
| Architecture | Native multimodal open transformer with expanded context | Extreme-context sparse MoE multimodal transformer (17B active / 109B total) |
| Parameters | 1B / 4B / 12B / 27B | 17B (109B total 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) · 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) · 11 GB VRAM (4-bit Quantized) · 37 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, vision |
| Output Modalities | text | text |
Gemma 3 vs Llama 4 Scout: よくある質問 (FAQ)
Which is more intelligent, Gemma 3 or Llama 4 Scout?
Llama 4 Scout is more intelligent. It scores 76 compared with 68 on the composite Intelligence Index.
Which model is faster?
Gemma 3 is faster, generating 105 tokens per second compared with 95 tokens per second.
Which model has lower latency?
Gemma 3 has lower latency, with a time to first token of 0.32s compared with 0.65s.
Which model has a larger context window?
Llama 4 Scout has a larger context window, supporting 3.5M tokens compared with 131.1K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Gemma 3 if your priority is Self-hosted multimodal applications, local image QA, and long document parsing. Its performance in demanding tasks justifies its operational token cost. Choose Llama 4 Scout if your workflow prioritizes Whole-repository code analysis, multi-hour video understanding, and legal book ingestion. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Gemma 3 compare to Llama 4 Scout in API token pricing and cost per token?+
Both Gemma 3 and Llama 4 Scout 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 3 or Llama 4 Scout?+
Gemma 3 supports a context window of 131K tokens (~98k words), while Llama 4 Scout supports 3,500K tokens (~2,625k words). Therefore, Llama 4 Scout can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemma 3 or Llama 4 Scout?+
Gemma 3 scores at the frontier of coding benchmarks, while Llama 4 Scout scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemma 3 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemma 3 and Llama 4 Scout?+
Gemma 3 is optimized for low-latency direct generation. In contrast, Llama 4 Scout focuses on standard fast inference.
How does inference speed (tokens per second) compare between Gemma 3 and Llama 4 Scout?+
Gemma 3 generates approximately 105 tokens per second, while Llama 4 Scout delivers 95 tokens per second. In latency-sensitive production environments, Gemma 3 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemma 3 and Llama 4 Scout 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 3, and route deep architectural or complex reasoning queries to Gemma 3. 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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs