Gemma 3n vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemma 3n by Google against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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Gemma 3n
音声・スピーチAIモデルGoogle の Gemma 3n は、自然な音声合成（TTS）、リアルタイム音声対話、高精度な文字起こし向けに設計されたAIモデルです。32K のコンテキストウィンドウ、毎秒 150 トークンの生成速度、知能指数 52/100（無料のオープンウェイト提供）を実現しています。
$0.00 / 1M
$0.00 / 1M
32K tokens
N/A
GPT-6 Astra
音声・スピーチAIモデルOpenAI の GPT-6 Astra は、自然な音声合成（TTS）、リアルタイム音声対話、高精度な文字起こし向けに設計されたAIモデルです。272K のコンテキストウィンドウ、毎秒 100 トークンの生成速度、知能指数 96/100（1Mトークンあたり入力$10・出力$50）を実現しています。
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
ひと目でわかる主な違い
- Gemma 3n is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 32K tokens).
- Gemma 3n is faster at 150 tok/s vs 100 tok/s.
| Feature | Gemma 3n | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 96 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 32K tokens~48 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 150 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.4% |
GPQA Diamond Graduate-level reasoning | N/A | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $1.00 / 1M |
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.
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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 3n · Strengths & Key Advantages
- 32K のコンテキストウィンドウと最大 4K トークンの出力容量（MMLU-Pro 64% · Arena Elo 1220）
- 毎秒 150 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 220ms の低遅延
- オープンウェイトモデル (2B / 5B)として vLLM・SGLang・Ollama を用いた自社GPUでのローカル運用が可能
Considerations & Trade-offs
- 非量子化のフル精度でローカル推論を行うには十分なGPU VRAM容量が必要です
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
GPT-6 Astra · Strengths & Key Advantages
- 272K のコンテキストウィンドウと最大 128K トークンの出力容量（SWE-bench Verified 82.4% · GPQA Diamond 80.9%）
- 毎秒 100 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 950ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$10・出力$50）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 大量のトークンを処理する本番環境では、プロンプトキャッシュや Batch API の併用によるコスト最適化が推奨されます
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 3n | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 3K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 3n | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-3n | gpt-6-astra |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-06 | 2026-09 |
| Knowledge Cutoff | March 2025 | June 2026 |
| Architecture | Edge-optimized multimodal transformer with audio support | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 2B / 5B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) · Audio (~25–32 tokens/sec) |
| 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, audio | text, vision, audio |
| Output Modalities | text | text, audio |
Gemma 3n vs GPT-6 Astra: よくある質問 (FAQ)
Which is more intelligent, Gemma 3n or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 52 on the composite Intelligence Index.
Which model is faster?
Gemma 3n is faster, generating 150 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Gemma 3n is cheaper at $0.00 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemma 3n has lower latency, with a time to first token of 0.22s compared with 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 32K tokens.
総合評価
Choose Gemma 3n for 自然な音声合成（tts）、リアルタイム音声対話、高精度な文字起こし、エンタープライズrag基盤、スケーラブルな本番api統合に最適。, or choose GPT-6 Astra for 自然な音声合成（tts）、リアルタイム音声対話、高精度な文字起こし、エンタープライズrag基盤、スケーラブルな本番api統合に最適。.
よくある質問 (FAQ)
Which is cheaper: Gemma 3n or GPT-6 Astra?+
Gemma 3n costs $0/1M input and $0/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Gemma 3n or GPT-6 Astra?+
Gemma 3n supports 32K tokens whereas GPT-6 Astra supports 272K tokens.
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: Google & OpenAI Official Documentation