Pixtral 12B vs Qwen 2.5 32B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Pixtral 12B de Mistral AI y Qwen 2.5 32B Instruct de Alibaba compiten directamente en el ecosistema actual de IA. Pixtral 12B ofrece una ventana de contexto de 128K tokens a $0.15/$0.15 por millón de tokens (entrada/salida), mientras que Qwen 2.5 32B Instruct ofrece 131.1K tokens de contexto a $0.18/$0.36 por millón de tokens. Analizamos sus benchmarks, latencia y costos reales.
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Pixtral 12B
IA Multimodal NativaPixtral 12B de Mistral AI es un modelo de IA diseñado para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, con ventana de contexto de 128K, velocidad de 85 tokens/s, índice de inteligencia de 40/100 y tarifa
$0.15 / 1M
$0.15 / 1M
128K tokens
28%
Qwen 2.5 32B Instruct
Modelo Base de Pesos AbiertosQwen 2.5 32B Instruct de Alibaba es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 131K, velocidad de 85 tokens/s, índice de inteligencia de 41
$0.18 / 1M
$0.36 / 1M
131.1K tokens
40.8%
Diferencias Clave de un Vistazo
- Pixtral 12B is 1.5x cheaper for API usage (input + output tokens combined).
- Qwen 2.5 32B Instruct has a larger context window (131K vs 128K tokens).
- Qwen 2.5 32B Instruct is faster at 85 tok/s vs 85 tok/s.
- Qwen 2.5 32B Instruct leads on SWE-bench coding (40.8% vs 28.0%).
| Característica | Pixtral 12B | Qwen 2.5 32B Instruct |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 40 | 41 |
Provider | Mistral AI | Alibaba |
Input Price Cost per million input tokens | $0.15 / 1M | $0.18 / 1M |
Output Price Cost per million output tokens | $0.15 / 1M | $0.36 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 85 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 28% | 40.8% |
GPQA Diamond Graduate-level reasoning | 43.8% | 48.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.04 / 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.
Pixtral 12B · Strengths & Key Advantages
- Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 28% · GPQA Diamond 43.8%)
- Rendimiento ágil de 85 tokens/segundo con latencia inicial (TTFT) de solo 450 ms
- Pesos abiertos (12B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Qwen 2.5 32B Instruct · Strengths & Key Advantages
- Ventana de contexto de 131K con hasta 8K tokens de salida máxima (SWE-bench Verified 40.8% · GPQA Diamond 48.5%)
- Rendimiento ágil de 85 tokens/segundo con latencia inicial (TTFT) de solo 480 ms
- Pesos abiertos (32B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Pixtral 12B | Qwen 2.5 32B Instruct |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $0.16 |
Output Tokens / Task Tokens generated per task | 25K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Pixtral 12B | Qwen 2.5 32B Instruct |
|---|---|---|
| Official API Model ID (SDK) | pixtral-12b-latest | qwen-2-5-32b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-09 | 2024-09 |
| Knowledge Cutoff | August 2024 | June 2024 |
| Architecture | Multimodal Vision-Language Transformer | Dense Autoregressive Transformer |
| Parameters | 12B | 32B |
| 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 ($0.15/1M in) across full 128K window | Flat rate ($0.18/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Cached input at $0.036/1M tokens |
| 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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, vision | text |
| Output Modalities | text | text |
Pixtral 12B vs Qwen 2.5 32B Instruct: Respuestas Rápidas (FAQ)
Which is more intelligent, Pixtral 12B or Qwen 2.5 32B Instruct?
Qwen 2.5 32B Instruct is more intelligent. It scores 41 compared with 40 on the composite Intelligence Index.
Which model is cheaper to run?
Pixtral 12B is cheaper at $0.08 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Pixtral 12B has lower latency, with a time to first token of 0.45s compared with 0.48s.
Which model has a larger context window?
Qwen 2.5 32B Instruct has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Veredicto
Tanto Pixtral 12B (Ideal para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, sistemas RAG empresariales y automatización mediante API.) como Qwen 2.5 32B Instruct (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen 2.5 32B Instruct lidera en inteligencia general y programación, Pixtral 12B ofrece mayor velocidad de generación de tokens y Pixtral 12B resulta más rentable para cargas de trabajo API de alto volumen.
Preguntas frecuentes
¿Cuál es mejor para programación y razonamiento, Pixtral 12B o Qwen 2.5 32B Instruct?+
En las pruebas de inteligencia compuesta y resolución de código, Qwen 2.5 32B Instruct obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.
¿Cuánto cuesta la API de Pixtral 12B frente a Qwen 2.5 32B Instruct?+
Pixtral 12B cuesta $0.15 / $0.15 por millón de tokens de entrada/salida, mientras que Qwen 2.5 32B Instruct cuesta $0.18 / $0.36. En costo combinado, Pixtral 12B es la opción más económica.
¿Qué modelo tiene mayor ventana de contexto y menor latencia?+
Pixtral 12B cuenta con 128K tokens de contexto frente a 131.1K tokens de Qwen 2.5 32B Instruct. En velocidad de inferencia, Pixtral 12B ofrece un rendimiento superior.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Llama 3.2 1B vs. Qwen 2.5 32B Instruct: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Pixtral 12B vs. Mistral Large 2: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Pixtral Large vs. Pixtral 12B: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Qwen 2.5 32B Instruct vs. Qwen 2.5 7B Instruct: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Qwen 2.5 72B vs. Qwen 2.5 32B Instruct: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Aion-3.0 vs. Aion-Mini: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
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: Documentación oficial de API de Mistral AI y Alibaba, tarjetas de sistema y benchmarks independientes (2026).