Mistral Large 2 vs GPT-4o
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
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.
بدّل وقارن بين أي نموذجين
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Mistral Large 2
نموذج مفتوح الأوزاننموذج Mistral Large 2 المقدم من Mistral AI يوفر نافذة سياق تصل إلى 128K رمز، بتكلفة $2 لكل مليون رمز إدخال و$6 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$2.00 / 1M
$6.00 / 1M
128K tokens
38%
GPT-4o
نموذج رائد متقدمنموذج GPT-4o المقدم من OpenAI يوفر نافذة سياق تصل إلى 128K رمز، بتكلفة $2.5 لكل مليون رمز إدخال و$10 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$2.50 / 1M
$10.00 / 1M
128K tokens
38.8%
أهم الفروقات في لمحة
- 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 |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Large 2 · Strengths & Key Advantages
- بنية 123B Dense Frontier Transformer متطورة من Mistral AI مع سعة سياق تبلغ 128K رمز
- كفاءة عالية في التكلفة ($2/1M إدخال و$6/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
GPT-4o · Strengths & Key Advantages
- بنية Omni-Multimodal Dense متطورة من OpenAI مع سعة سياق تبلغ 128K رمز
- كفاءة عالية في التكلفة ($2.5/1M إدخال و$10/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
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 |
المواصفات التقنية
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: إجابات سريعة (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.
الحكم النهائي
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.
الأسئلة الشائعة (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.
Related reading
- Claude 3.5 Sonnet vs GPT-4o: Benchmarks, Pricing & Speed Comparisonمقارنة
- Claude 3.7 Sonnet vs GPT-4o: Benchmarks, Pricing & Speed Comparisonمقارنة
- DeepSeek V3 vs GPT-4o: Benchmarks, Pricing & Speed Comparisonمقارنة
- GPT-4o vs Gemini 1.5 Pro: Benchmarks, Pricing & Speed Comparisonمقارنة
- GPT-4o vs GPT-4 Turbo: Benchmarks, Pricing & Speed Comparisonمقارنة
- Grok 2 vs GPT-4o: Benchmarks, Pricing & Speed Comparisonمقارنة
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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs