Head-to-Head Model Comparison

Step 3.7 Flash vs Command A Plus

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

Cambia y Compara Cualquier Modelo

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Step 3.7 FlashProfile

Command A PlusProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

StepFun

Step 3.7 Flash

Inferencia Ultrarrápida

Step 3.7 Flash de StepFun es un modelo de IA diseñado para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, con ventana de contexto de 256K, velocidad de 160 tokens/s, índice de inteligencia de 76/100 y tarifa de ac

Intelligence Index76 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

N/A

Cohere

Command A Plus

Modelo Base de Pesos Abiertos

Command A Plus 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 68 tokens/s, índice de inteligencia de 58/100 y t

Intelligence Index58 / 100

Input Token:

$3.00 / 1M

Output Token:

$12.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

60.5%

Diferencias Clave de un Vistazo

Step 3.7 Flash is 1.0x cheaper for API usage (input + output tokens combined).
Step 3.7 Flash has a larger context window (256K vs 128K tokens).
Step 3.7 Flash is faster at 160 tok/s vs 68 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	Step 3.7 Flash	Command A Plus
Intelligence Index Composite reasoning & knowledge score (0-100)	76	58
Provider	StepFun	Cohere
Input Price Cost per million input tokens	$0.00 / 1M	$3.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$12.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$2.33 / 1M
Context Window Maximum input + output tokens	256K tokens~384 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	8K	33K
Throughput Speed Tokens generated per second	160 tok/s	68 tok/s
SWE-bench Verified Real-world software engineering	N/A	60.5%
GPQA Diamond Graduate-level reasoning	N/A	74%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.75 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Evaluations ongoing

Costo

Step 3.7 Flash

1.0x cheaper monthly runtime

Velocidad

Step 3.7 Flash

+135% faster generation

Contexto

Step 3.7 Flash

256K tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Step 3.7 FlashCommand A Plus

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs60.5%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs74%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

73%vs86.8%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1275ptsvs1410pts

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs50%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs82.5%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs88%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs68 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Step 3.7 Flash0.25s

Command A Plus0.56s

Est. End-to-End Response (500 tokens)TTFT + generation time

Step 3.7 Flash3.38s

Command A Plus7.91s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Step 3.7 Flash Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Command A Plus Monthly API Bill

$16.43 / month

$3/1M in · $12/1M out

Step 3.7 Flash saves approximately $16.43 / month (100%) under this workload.1.0x Savings

Step 3.7 Flash · Strengths & Key Advantages

Ventana de contexto de 256K con hasta 8K tokens de salida máxima (MMLU-Pro 73% · Arena Elo 1275)
Rendimiento ágil de 160 tokens/segundo con latencia inicial (TTFT) de solo 250 ms
Pesos abiertos (Confidential / 70B MoE) 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

Command A Plus · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 33K tokens de salida máxima (SWE-bench Verified 60.5% · GPQA Diamond 74%)
Rendimiento ágil de 68 tokens/segundo con latencia inicial (TTFT) de solo 560 ms
Pesos abiertos (Confidential (~180B+ MoE)) 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 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	Step 3.7 Flash	Command A Plus
Cost per Task Estimated USD to finish one task	$0.00	$0.85
Output Tokens / Task Tokens generated per task	12K	45K
Reasoning Tokens / Task Thinking tokens burned per task	2K	22K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Step 3.7 Flash	Command A Plus
Official API Model ID (SDK)	step-3-7-flash	command-a-plus
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-05	2026-05
Knowledge Cutoff	March 2026	March 2026
Architecture	High-Velocity Open Reasoning MoE	Flagship Multimodal Agentic MoE
Parameters	Confidential / 70B MoE	Confidential (~180B+ MoE)
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)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($3/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.75/1M 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)	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) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 112 GB VRAM (4-bit Quantized) · 387 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, vision
Output Modalities	text	text

Step 3.7 Flash vs Command A Plus: Respuestas Rápidas (FAQ)

Which is more intelligent, Step 3.7 Flash or Command A Plus?

Step 3.7 Flash is more intelligent. It scores 76 compared with 58 on the composite Intelligence Index.

Which model is faster?

Step 3.7 Flash is faster, generating 160 tokens per second compared with 68 tokens per second.

Which model is cheaper to run?

Step 3.7 Flash is cheaper at $0.00 per 1M tokens blended, compared with $2.33 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Step 3.7 Flash has lower latency, with a time to first token of 0.25s compared with 0.56s.

Which model has a larger context window?

Step 3.7 Flash has a larger context window, supporting 256K tokens compared with 128K tokens.

Veredicto

Tanto Step 3.7 Flash (Ideal para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, sistemas RAG empresariales y automatización mediante API.) como Command A Plus (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Step 3.7 Flash lidera en inteligencia general y programación, Step 3.7 Flash ofrece mayor velocidad de generación de tokens y Step 3.7 Flash resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Step 3.7 Flash o Command A Plus?+

En las pruebas de inteligencia compuesta y resolución de código, Step 3.7 Flash obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Step 3.7 Flash frente a Command A Plus?+

Step 3.7 Flash cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que Command A Plus cuesta $3.00 / $12.00. En costo combinado, Step 3.7 Flash es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Step 3.7 Flash cuenta con 256K tokens de contexto frente a 128K tokens de Command A Plus. En velocidad de inferencia, Step 3.7 Flash ofrece un rendimiento superior.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Step 3.7 Flash vs Command A Plus

Cambia y Compara Cualquier Modelo

Step 3.7 Flash

Command A Plus

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Step 3.7 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Command A Plus · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Step 3.7 Flash vs Command A Plus: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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