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
오픈 웨이트 파운데이션 모델Mistral AI의 Mixtral 8x7B 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 33K 컨텍스트 윈도우, 초당 130 토큰의 생성 속도, 종합 지능 지수 37/100점(1M 토큰당 입력 $0.2 / 출력 $0.2)을 제공합니다.
$0.20 / 1M
$0.20 / 1M
32.8K tokens
N/A
Gemini 4 Argon
네이티브 멀티모달 AIGoogle의 Gemini 4 Argon 모델은 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 115 토큰의 생성 속도, 종합 지능 지수 97/100점(1M 토큰당 입력 $2 / 출력 $10)을 제공합니다.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
한눈에 보는 핵심 차이점
- 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 |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mixtral 8x7B · Strengths & Key Advantages
- 33K 컨텍스트 윈도우 및 최대 4K 출력 토큰 생성 지원 (GPQA Diamond 38% · MMLU-Pro 66.5%)
- 초당 130 토큰의 빠른 처리 속도와 380ms에 불과한 첫 토큰 지연 시간(TTFT)
- 오픈 웨이트 아키텍처 (46.7B (12.9B ))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
- 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
- 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다
Gemini 4 Argon · Strengths & Key Advantages
- 1M 컨텍스트 윈도우 및 최대 1M 출력 토큰 생성 지원 (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
- 초당 115 토큰의 빠른 처리 속도와 850ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $2 / 출력 $10)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
- 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
- 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다
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 |
기술 사양 (Specifications)
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: 핵심 요약 문답 (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.
종합 평가
Choose Mixtral 8x7B for 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Gemini 4 Argon for 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..
자주 묻는 질문 (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.
공식 출처: Mistral AI & Google Official Documentation