Mistral Large 2 vs Llama 3.3 70B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Mistral Large 2 by Mistral AI and Llama 3.3 70B by Meta 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, Llama 3.3 70B holds a notable pricing advantage for token ingestion. On output generation, Llama 3.3 70B 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, Llama 3.3 70B 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.
Cambia y Compara Cualquier Modelo
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Mistral Large 2
Buque insignia de frontera europeoEl modelo insignia europeo de frontera con ventana de contexto de 128K, sólido soporte multilingüe y rendimiento excepcional en codificación.
$2.00 / 1M
$6.00 / 1M
128K tokens
38%
Llama 3.3 70B
Titán de pesos abiertosEl modelo fundacional de pesos abiertos insignia de Meta que ofrece un rendimiento un la par de los modelos anteriores de 405B con mucho menor costo computacional.
$0.00 / 1M
$0.00 / 1M
128K tokens
39.5%
Diferencias Clave de un Vistazo
- Llama 3.3 70B is 66.0x cheaper for API usage (input + output tokens combined).
- Llama 3.3 70B is faster at 280 tok/s vs 75 tok/s.
- Llama 3.3 70B leads on SWE-bench coding (39.5% vs 38.0%).
| Característica | Mistral Large 2 | Llama 3.3 70B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 38 |
Provider | Mistral AI | Meta |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.70 / 1M | $0.07 / 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 | 4K |
Throughput Speed Tokens generated per second | 75 tok/s | 280 tok/s |
SWE-bench Verified Real-world software engineering | 38% | 39.5% |
GPQA Diamond Graduate-level reasoning | 54% | 48.9% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 1M | $0.10 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Large 2 · Strengths & Key Advantages
- Directly rivals GPT-4o y Claude 3.5 Sonnet en razonamiento y generación de código
- denso 123B parámetro modelo optimizado para único-node servicio eficiencia
- primer-clase JSON schema outputs y multipaso agentic function calling
Considerations & Trade-offs
- texto-solo sin nativo imagen visión capacidades
- requiere empresarial multi-GPU server para local hosting
Llama 3.3 70B · Strengths & Key Advantages
- 100% de pesos abiertos allowing completo datos soberanía, privado hosting, y ajuste fino
- Lightning-rápido rendimiento (up un 280 tok/s en especializado hardware inferencia providers)
- Economical cloud API precios ($0.20 entrada / $0.70 salida por millones tokens)
- Industry estándar para local offline AI y empresarial cumplimiento
Considerations & Trade-offs
- Knowledge cutoff en late 2023 requiere RAG para recent events
- Smaller maximum salida token generación limit (4K tokens)
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Large 2 | Llama 3.3 70B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.21 |
Output Tokens / Task Tokens generated per task | 24K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Mistral Large 2 | Llama 3.3 70B |
|---|---|---|
| Official API Model ID (SDK) | mistral-large-2-latest | meta-llama/Llama-3.3-70B |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-07 | 2024-12 |
| Knowledge Cutoff | July 2024 | December 2023 |
| Architecture | 123B Dense Frontier Transformer | Dense Autoregressive Transformer |
| Parameters | 123B | 70B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 128K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $1/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Mistral Large 2 vs Llama 3.3 70B: Respuestas Rápidas (FAQ)
Which is more intelligent, Mistral Large 2 or Llama 3.3 70B?
Mistral Large 2 is more intelligent. It scores 46 compared with 38 on the composite Intelligence Index.
Which model is faster?
Llama 3.3 70B is faster, generating 280 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Llama 3.3 70B is cheaper at $0.07 per 1M tokens blended, compared with $1.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.52s.
Veredicto
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 Llama 3.3 70B if your workflow prioritizes Private on-premises deployments, high-throughput text summarization, and fine-tuned domain tasks. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does Mistral Large 2 compare to Llama 3.3 70B 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 Llama 3.3 70B costs $0.00 input and $0.00 output. Overall, Llama 3.3 70B 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 Llama 3.3 70B?+
Both Mistral Large 2 and Llama 3.3 70B 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 Llama 3.3 70B?+
Mistral Large 2 scores 38% on SWE-bench Verified while Llama 3.3 70B scores 39.5%. For autonomous software engineering and multi-file debugging, Llama 3.3 70B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mistral Large 2 and Llama 3.3 70B?+
Mistral Large 2 is optimized for low-latency direct generation. In contrast, Llama 3.3 70B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Mistral Large 2 and Llama 3.3 70B?+
Mistral Large 2 generates approximately 75 tokens per second, while Llama 3.3 70B delivers 280 tokens per second. In latency-sensitive production environments, Llama 3.3 70B 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 Llama 3.3 70B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.3 70B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Mistral Large 2. 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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs