Mistral 7B vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Mistral 7B 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.
Mistral 7B
Milagro de eficiencia de 7Bel Parisian de lab legendary breakout 7B modelo utilizando Sliding Window Attention un outperform Llama 2 13B un través de every benchmark bajo Apache 2.0.
$0.06 / 1M
$0.06 / 1M
32.8K tokens
N/A
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
- Mistral 7B is 73.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 33K tokens).
- Mistral 7B is faster at 200 tok/s vs 120 tok/s.
| Característica | Mistral 7B | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 32 | 94 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.06 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.06 / 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 | 32.8K tokens~49 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 200 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones 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 7B · Strengths & Key Advantages
- Sliding Window Attention (SWA) handles 32K contexto con reduced compute overhead
- Completely unrestricted Apache 2.0 de pesos abiertos license
- ligero huella easily runnable en laptops y phones
Considerations & Trade-offs
- pequeño 7B capacidad limits multipaso complejo algorithmic razonamiento
- Lacked llamada un herramientas en initial raw weights lanzamiento
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 | Mistral 7B | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $2.05 |
Output Tokens / Task Tokens generated per task | 24K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Mistral 7B | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | mistral-7b-latest | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2023-09 | 2026-09 |
| Knowledge Cutoff | Mid 2023 | April 2026 |
| Architecture | Sliding Window Attention (SWA) Transformer | Near-Astra unified reasoning model |
| Parameters | 7.3B | 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.06/1M in) across full 33K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.015/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | 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) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 16 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 |
| Output Modalities | text | text |
Mistral 7B vs GPT-6.1 Sol: Respuestas Rápidas (FAQ)
Which is more intelligent, Mistral 7B or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 32 on the composite Intelligence Index.
Which model is faster?
Mistral 7B is faster, generating 200 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Mistral 7B 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?
Mistral 7B has lower latency, with a time to first token of 0.24s 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 32.8K tokens.
Veredicto
Choose Mistral 7B for local embedded aplicaciones, alto-rendimiento texto clasificación, y ligero en-premise summarizers, or choose GPT-6.1 Sol for el mejor valor modelo de frontera para profesional y agentic trabajo.
Preguntas frecuentes
Which is cheaper: Mistral 7B or GPT-6.1 Sol?+
Mistral 7B costs $0.06/1M input and $0.06/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Mistral 7B or GPT-6.1 Sol?+
Mistral 7B supports 33K 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