Gemini 4 Argon vs Aya Expanse 8B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 4 Argon by Google against Aya Expanse 8B by Cohere across real-world coding, reasoning, token economics, and latency.
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Gemini 4 Argon
高度な推論モデルGoogle が提供する Gemini 4 Argon は、最大 1M トークンのコンテキストウィンドウを備え、入力 $2/100万トークン・出力 $10/100万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。
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Aya Expanse 8B
オープンウェイトモデルCohere が提供する Aya Expanse 8B は、最大 8K トークンのコンテキストウィンドウを備え、入力 $0.08/100万トークン・出力 $0.16/100万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。
$0.08 / 1M
$0.16 / 1M
8.2K tokens
24%
ひと目でわかる主な違い
- Aya Expanse 8B is 41.3x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 8K tokens).
- Aya Expanse 8B is faster at 135 tok/s vs 115 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 24.0%).
| Feature | Gemini 4 Argon | Aya Expanse 8B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 97 | 36 |
Provider | Cohere | |
Input Price Cost per million input tokens | $2.00 / 1M | $0.08 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.16 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $0.05 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 8.2K tokens~12 A4 pages of text |
Max Output Tokens Per single request | 1000K | 4K |
Throughput Speed Tokens generated per second | 115 tok/s | 135 tok/s |
SWE-bench Verified Real-world software engineering | 81.9% | 24% |
GPQA Diamond Graduate-level reasoning | 81.5% | 40% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.02 / 1M |
分野別勝者スコアカード
At-a-glance decision framework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.
Gemini 4 Argon · Strengths & Key Advantages
- Google の高度な Pathways Multimodal Sparse Mixture-of-Experts アーキテクチャと 1M 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 $2/1M、出力 $10/1M）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
Aya Expanse 8B · Strengths & Key Advantages
- Cohere の高度な Compact Multilingual Dense Transformer アーキテクチャと 8K 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 $0.08/1M、出力 $0.16/1M）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 | Gemini 4 Argon | Aya Expanse 8B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.45 | $0.04 |
Output Tokens / Task Tokens generated per task | 75K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 4 Argon | Aya Expanse 8B |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-4-argon | aya-expanse-8b |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2024-10 |
| Knowledge Cutoff | June 2026 | August 2024 |
| Architecture | Pathways Multimodal Sparse Mixture-of-Experts | Compact Multilingual Dense Transformer |
| Parameters | Confidential | 8B |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($0.08/1M in) across full 8K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL | Cached input at $0.02/1M tokens |
| 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 | Vertex AI Supervised Adapter Tuning & Context Distillation | 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) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 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) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision, audio, video | text |
| Output Modalities | text | text |
Gemini 4 Argon vs Aya Expanse 8B: よくある質問 (FAQ)
Which is more intelligent, Gemini 4 Argon or Aya Expanse 8B?
Gemini 4 Argon is more intelligent. It scores 97 compared with 36 on the composite Intelligence Index.
Which model is faster?
Aya Expanse 8B is faster, generating 135 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Aya Expanse 8B is cheaper at $0.05 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aya Expanse 8B has lower latency, with a time to first token of 0.30s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 8.2K tokens.
総合評価
Choose Gemini 4 Argon for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、google エコシステムの統合, or choose Aya Expanse 8B for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、cohere エコシステムの統合.
よくある質問 (FAQ)
Which is cheaper: Gemini 4 Argon or Aya Expanse 8B?+
Gemini 4 Argon costs $2/1M input and $10/1M output tokens, while Aya Expanse 8B costs $0.08/1M input and $0.16/1M output tokens.
Which model has a larger context window: Gemini 4 Argon or Aya Expanse 8B?+
Gemini 4 Argon supports 1M tokens whereas Aya Expanse 8B supports 8K 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 & Cohere Official Documentation