Command R7B vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Command R7B by Cohere against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Live comparison: no in-depth editorial review has been published for this pair yet.
Command R7B
Modelo Base de Pesos AbiertosCommand R7B de Cohere es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 140 tokens/s, índice de inteligencia de 38/100 y tar
$0.07 / 1M
$0.20 / 1M
128K tokens
27%
GPT-6.1 Sol
Modelo Insignia de FronteraGPT-6.1 Sol de OpenAI es un modelo de IA diseñado para razonamiento empresarial de frontera, programación avanzada y flujos de producción, con ventana de contexto de 1M, velocidad de 120 tokens/s, índice de inteligencia de 94/100 y tarifa d
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Diferencias Clave de un Vistazo
- Command R7B is 38.6x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 128K tokens).
- Command R7B is faster at 140 tok/s vs 120 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 27.0%).
| Característica | Command R7B | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 94 |
Provider | Cohere | OpenAI |
Input Price Cost per million input tokens | $0.07 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.20 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 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 | 4K | 128K |
Throughput Speed Tokens generated per second | 140 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 27% | 82.3% |
GPQA Diamond Graduate-level reasoning | 42.5% | 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.
Command R7B · Strengths & Key Advantages
- Ventana de contexto de 128K con hasta 4K tokens de salida máxima (SWE-bench Verified 27% · GPQA Diamond 42.5%)
- Rendimiento ágil de 140 tokens/segundo con latencia inicial (TTFT) de solo 290 ms
- Pesos abiertos (7B) 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
GPT-6.1 Sol · Strengths & Key Advantages
- Ventana de contexto de 1M con hasta 128K tokens de salida máxima (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
- Rendimiento ágil de 120 tokens/segundo con latencia inicial (TTFT) de solo 1120 ms
- Economía de API competitiva ($2/$10 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas
Considerations & Trade-offs
- Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
- En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Command R7B | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $2.05 |
Output Tokens / Task Tokens generated per task | 20K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Command R7B | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | command-r7b | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-12 | 2026-09 |
| Knowledge Cutoff | October 2024 | April 2026 |
| Architecture | Efficient Compact RAG Transformer | Near-Astra unified reasoning model |
| Parameters | 7B | 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 ($0.07/1M 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 | 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) · 4 GB VRAM (4-bit Quantized) · 15 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 |
Command R7B vs GPT-6.1 Sol: Respuestas Rápidas (FAQ)
Which is more intelligent, Command R7B or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 38 on the composite Intelligence Index.
Which model is faster?
Command R7B is faster, generating 140 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Command R7B is cheaper at $0.05 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Command R7B has lower latency, with a time to first token of 0.29s 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 Command R7B for ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas rag empresariales y automatización mediante api., or choose GPT-6.1 Sol for ideal para razonamiento empresarial de frontera, programación avanzada y flujos de producción, sistemas rag empresariales y automatización mediante api..
Preguntas frecuentes
Which is cheaper: Command R7B or GPT-6.1 Sol?+
Command R7B costs $0.07/1M input and $0.2/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Command R7B or GPT-6.1 Sol?+
Command R7B 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: Cohere & OpenAI Official Documentation