Ministral 3B vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Ministral 3B by Mistral AI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
Cambia y Compara Cualquier Modelo
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Live comparison: no in-depth editorial review has been published for this pair yet.
Ministral 3B
Modelo de código abiertoModelo Ministral 3B desarrollado por Mistral AI, optimizado para alto rendimiento y confiabilidad en producción.
$0.04 / 1M
$0.04 / 1M
128K tokens
22.1%
GPT-6.1 Sol
Valor cercano a Astraofrece results close un GPT-6 Astra un roughly one fifth del precio — el latest 2026 lanzamiento.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Diferencias Clave de un Vistazo
- Ministral 3B is 96.4x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 128K tokens).
- Ministral 3B is faster at 180 tok/s vs 120 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 22.1%).
| Característica | Ministral 3B | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 33 | 94 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.04 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.04 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 180 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 22.1% | 82.3% |
GPQA Diamond Graduate-level reasoning | 32.5% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 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.
Ministral 3B · Strengths & Key Advantages
- Arquitectura optimizada por Mistral AI para alta velocidad de inferencia
- Excelente soporte para salidas estructuradas JSON y llamadas a herramientas
- Estabilidad verificada en cargas de trabajo de producción empresarial
Considerations & Trade-offs
- Requiere infraestructura dedicada o claves de API para alto volumen
- Se debe supervisar la ventana de contexto en documentos masivos
GPT-6.1 Sol · Strengths & Key Advantages
- Near-Astra results un roughly un fifth del costo
- Excellent codificación y de largo alcance agente rendimiento
- sólido reliability con far menor latencia que Astra
- Near-insignia results un roughly un fifth del precio
Considerations & Trade-offs
- Still marginally behind Astra en el muy hardest razonamiento
- Same precio as GPT-6 Sol, so el upgrade decision es easy
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Ministral 3B | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $2.05 |
Output Tokens / Task Tokens generated per task | 15K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Ministral 3B | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | ministral-3b-latest | gpt-6-1-sol |
| API Protocol | Mistral AI REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-10 | 2026-09 |
| Knowledge Cutoff | October 2024 | April 2026 |
| Architecture | Ultra-Compact Dense Transformer | Near-Astra unified reasoning model |
| Parameters | 3B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.04/1M in) across full 128K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Automatic prefix caching at $0.1/1M (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 | API Prompt Engineering & RAG Distillation | 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) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Mistral AI Production Safety & Alignment Verified | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Ministral 3B vs GPT-6.1 Sol: Respuestas Rápidas (FAQ)
Which is more intelligent, Ministral 3B or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 33 on the composite Intelligence Index.
Which model is faster?
Ministral 3B is faster, generating 180 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Ministral 3B is cheaper at $0.03 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Ministral 3B has lower latency, with a time to first token of 0.21s compared with 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 128K tokens.
Veredicto
Choose Ministral 3B for desarrollo empresarial, automatización y análisis de datos con tecnología de mistral ai, or choose GPT-6.1 Sol for el mejor valor modelo de frontera para profesional y agentic trabajo.
Preguntas frecuentes
Which is cheaper: Ministral 3B or GPT-6.1 Sol?+
Ministral 3B costs $0.04/1M input and $0.04/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Ministral 3B or GPT-6.1 Sol?+
Ministral 3B supports 128K tokens whereas GPT-6.1 Sol 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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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: Mistral AI & OpenAI Official Documentation