Mistral Large 2 vs GPT-4o
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Mistral Large 2 by Mistral AI and GPT-4o by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Mistral Large 2 holds a notable pricing advantage for token ingestion. On output generation, Mistral Large 2 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, GPT-4o leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Mistral Large 2
오픈 웨이트 모델Mistral AI의 Mistral Large 2 모델은 128K 토큰 컨텍스트 윈도우와 입력 $2/1M 토큰, 출력 $6/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$2.00 / 1M
$6.00 / 1M
128K tokens
38%
GPT-4o
플래그십 프론티어 모델OpenAI의 GPT-4o 모델은 128K 토큰 컨텍스트 윈도우와 입력 $2.5/1M 토큰, 출력 $10/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$2.50 / 1M
$10.00 / 1M
128K tokens
38.8%
한눈에 보는 핵심 차이점
- Mistral Large 2 is 1.5x cheaper for API usage (input + output tokens combined).
- GPT-4o is faster at 105 tok/s vs 75 tok/s.
- GPT-4o leads on SWE-bench coding (38.8% vs 38.0%).
| Feature | Mistral Large 2 | GPT-4o |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 41 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $2.00 / 1M | $2.50 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.70 / 1M | $2.38 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 75 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | 38% | 38.8% |
GPQA Diamond Graduate-level reasoning | 54% | 53.6% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 1M | $1.25 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 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.
Mistral Large 2 · Strengths & Key Advantages
- Mistral AI의 첨단 123B Dense Frontier Transformer 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $2/1M, 출력 $6/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
GPT-4o · Strengths & Key Advantages
- OpenAI의 첨단 Omni-Multimodal Dense 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $2.5/1M, 출력 $10/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Large 2 | GPT-4o |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.68 |
Output Tokens / Task Tokens generated per task | 24K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Large 2 | GPT-4o |
|---|---|---|
| Official API Model ID (SDK) | mistral-large-2-latest | gpt-4o |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-07 | 2024-05 |
| Knowledge Cutoff | July 2024 | October 2023 |
| Architecture | 123B Dense Frontier Transformer | Omni-Multimodal Dense |
| Parameters | 123B | 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) | Flat rate ($2/1M in) across full 128K window | Flat rate ($2.5/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $1/1M tokens | Automatic prefix caching at $1.25/1M (no extra cache-write fee) |
| 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 | 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) | 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) · 76 GB VRAM (4-bit Quantized) · 264 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) | 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 | text, vision, audio |
| Output Modalities | text | text, audio |
Mistral Large 2 vs GPT-4o: 핵심 요약 문답 (FAQ)
Which is more intelligent, Mistral Large 2 or GPT-4o?
Mistral Large 2 is more intelligent. It scores 46 compared with 41 on the composite Intelligence Index.
Which model is faster?
GPT-4o is faster, generating 105 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Mistral Large 2 is cheaper at $1.70 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4o has lower latency, with a time to first token of 0.41s compared with 0.52s.
종합 평가
Verdict & Model Selection Recommendation: Choose Mistral Large 2 if your priority is Enterprise sovereignty in the EU, complex multilingual reasoning, and autonomous code pipelines. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4o if your workflow prioritizes Omni-modal voice applications, conversational agents, and structured enterprise APIs. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does Mistral Large 2 compare to GPT-4o in API token pricing and cost per token?+
Mistral Large 2 costs $2.00 per 1M input tokens and $6.00 per 1M output tokens, whereas GPT-4o costs $2.50 input and $10.00 output. Overall, Mistral Large 2 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Mistral Large 2 or GPT-4o?+
Both Mistral Large 2 and GPT-4o offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Mistral Large 2 or GPT-4o?+
Mistral Large 2 scores 38% on SWE-bench Verified while GPT-4o scores 38.8%. For autonomous software engineering and multi-file debugging, GPT-4o holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mistral Large 2 and GPT-4o?+
Mistral Large 2 is optimized for low-latency direct generation. In contrast, GPT-4o focuses on standard fast inference.
How does inference speed (tokens per second) compare between Mistral Large 2 and GPT-4o?+
Mistral Large 2 generates approximately 75 tokens per second, while GPT-4o delivers 105 tokens per second. In latency-sensitive production environments, GPT-4o provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mistral Large 2 and GPT-4o to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Mistral Large 2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4o. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs