Mixtral 8x7B vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Mixtral 8x7B by Mistral AI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Mixtral 8x7B
オープンウェイト基盤モデルMistral AI の Mixtral 8x7B は、オンプレミス環境でのセルフホスト、ファインチューニング、完全なデータ主権の確保向けに設計されたAIモデルです。33K のコンテキストウィンドウ、毎秒 130 トークンの生成速度、知能指数 37/100（1Mトークンあたり入力$0.2・出力$0.2）を実現しています。
$0.20 / 1M
$0.20 / 1M
32.8K tokens
N/A
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
- Mixtral 8x7B is 21.9x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 33K tokens).
- Mixtral 8x7B is faster at 130 tok/s vs 115 tok/s.
| Feature | Mixtral 8x7B | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 37 | 97 |
Provider | Mistral AI | |
Input Price Cost per million input tokens | $0.20 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.20 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 1000K |
Throughput Speed Tokens generated per second | 130 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 81.9% |
GPQA Diamond Graduate-level reasoning | 38% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.10 / 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.
Mixtral 8x7B · Strengths & Key Advantages
- 33K 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（GPQA Diamond 38% · MMLU-Pro 66.5%）
- 毎秒 130 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 380ms の低遅延
- オープンウェイトモデル (46.7B (12.9B ))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
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 | Mixtral 8x7B | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $2.45 |
Output Tokens / Task Tokens generated per task | 24K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Mixtral 8x7B | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | mixtral-8x7b-latest | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2023-12 | 2026-09 |
| Knowledge Cutoff | Late 2023 | June 2026 |
| Architecture | Sparse Mixture of Experts (MoE, 2 active of 8) | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 46.7B (12.9B active) | 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 33K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 29 GB VRAM (4-bit Quantized) · 100 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Google DeepMind Frontier Safety Framework Verified |
| 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 | text, vision, audio, video |
| Output Modalities | text | text |
Mixtral 8x7B vs Gemini 4 Argon: よくある質問 (FAQ)
Which is more intelligent, Mixtral 8x7B or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 37 on the composite Intelligence Index.
Which model is faster?
Mixtral 8x7B is faster, generating 130 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Mixtral 8x7B is cheaper at $0.10 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Mixtral 8x7B has lower latency, with a time to first token of 0.38s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 32.8K tokens.
総合評価
Choose Mixtral 8x7B for オンプレミス環境でのセルフホスト、ファインチューニング、完全なデータ主権の確保、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: Mixtral 8x7B or Gemini 4 Argon?+
Mixtral 8x7B costs $0.2/1M input and $0.2/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Mixtral 8x7B or Gemini 4 Argon?+
Mixtral 8x7B supports 33K 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