Magistral Medium vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Magistral Medium by Mistral AI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Magistral Medium
ディープ推論・論理エンジンMistral AI の Magistral Medium は、高度な数学的推論、科学技術計算、多段階の思考連鎖（Chain-of-Thought）向けに設計されたAIモデルです。128K のコンテキストウィンドウ、毎秒 50 トークンの生成速度、知能指数 51/100（1Mトークンあたり入力$1・出力$3.5）を実現しています。
$1.00 / 1M
$3.50 / 1M
128K tokens
45.2%
Gemini 4 Argon
ネイティブ・マルチモーダルAIGoogle の Gemini 4 Argon は、テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析向けに設計されたAIモデルです。1M のコンテキストウィンドウ、毎秒 115 トークンの生成速度、知能指数 97/100（1Mトークンあたり入力$2・出力$10）を実現しています。
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
ひと目でわかる主な違い
- Magistral Medium is 2.4x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 128K tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 50 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 45.2%).
| Feature | Magistral Medium | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 51 | 97 |
Provider | Mistral AI | |
Input Price Cost per million input tokens | $1.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $3.50 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.72 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 1000K |
Throughput Speed Tokens generated per second | 50 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 45.2% | 81.9% |
GPQA Diamond Graduate-level reasoning | 67.8% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.10 / 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.
Magistral Medium · Strengths & Key Advantages
- 128K のコンテキストウィンドウと最大 33K トークンの出力容量（SWE-bench Verified 45.2% · GPQA Diamond 67.8%）
- 毎秒 50 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 850ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$1・出力$3.5）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
- 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
Gemini 4 Argon · Strengths & Key Advantages
- 1M のコンテキストウィンドウと最大 1M トークンの出力容量（SWE-bench Verified 81.9% · GPQA Diamond 81.5%）
- 毎秒 115 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 850ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$2・出力$10）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 | Magistral Medium | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.65 | $2.45 |
Output Tokens / Task Tokens generated per task | 40K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 36K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Magistral Medium | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | magistral-medium-latest | models/gemini-4-argon |
| API Protocol | Mistral AI REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-03 | 2026-09 |
| Knowledge Cutoff | January 2025 | June 2026 |
| Architecture | Reinforcement-Learned Reasoning Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential (~70B) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.25/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Mistral AI Production Safety & Alignment Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision, audio, video |
| Output Modalities | text | text |
Magistral Medium vs Gemini 4 Argon: よくある質問 (FAQ)
Which is more intelligent, Magistral Medium or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 51 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 50 tokens per second.
Which model is cheaper to run?
Magistral Medium is cheaper at $0.72 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.
総合評価
Choose Magistral Medium for 高度な数学的推論、科学技術計算、多段階の思考連鎖（chain-of-thought）、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 Gemini 4 Argon for テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析、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: Magistral Medium or Gemini 4 Argon?+
Magistral Medium costs $1/1M input and $3.5/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Magistral Medium or Gemini 4 Argon?+
Magistral Medium supports 128K tokens whereas Gemini 4 Argon supports 1M 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: Mistral AI & Google Official Documentation