Head-to-Head Model Comparison

Step 3.7 Flash vs Command A Plus

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Step 3.7 FlashProfile

Command A PlusProfile

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Popular Matchups:

StepFun

Step 3.7 Flash

Ultraschnelle Echtzeit-Inferenz

Step 3.7 Flash von StepFun ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 256K Kontextfenster, 160 Token/s Durchsatz, einem Intelligenz-Index von 76/100 und kostenlosem Open-Weights-Zug

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

Open-Weights-Basismodell

Command A Plus von Cohere ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 68 Token/s Durchsatz, einem Intelligenz-Index von 58/100 und $3/$12 pro 1M Token.

Intelligence Index58 / 100

Input Token:

$3.00 / 1M

Output Token:

$12.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

60.5%

Wichtigste Unterschiede auf einen Blick

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 →

Feature	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

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Step 3.7 Flash

1.0x cheaper monthly runtime

Speed

Step 3.7 Flash

+135% faster generation

Context

Step 3.7 Flash

256K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

256K Kontextfenster und bis zu 8K maximale Output-Token (MMLU-Pro 73% · Arena Elo 1275)
Hohe Generierungsgeschwindigkeit von 160 Token/Sekunde bei nur 250 ms Erstlatenz (TTFT)
Offene Modellgewichte (Confidential / 70B MoE) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Command A Plus · Strengths & Key Advantages

128K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 60.5% · GPQA Diamond 74%)
Hohe Generierungsgeschwindigkeit von 68 Token/Sekunde bei nur 560 ms Erstlatenz (TTFT)
Offene Modellgewichte (Confidential (~180B+ MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

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

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	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: Schnelle Antworten (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.

Fazit

Sowohl Step 3.7 Flash (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Command A Plus (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Step 3.7 Flash führt bei komplexem Reasoning und Coding, Step 3.7 Flash liefert den höheren Token-Durchsatz und Step 3.7 Flash ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Step 3.7 Flash oder Command A Plus?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Step 3.7 Flash vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Step 3.7 Flash und Command A Plus?+

Step 3.7 Flash kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Command A Plus liegt bei $3.00 / $12.00. Beim gewichteten Gesamtpreis ist Step 3.7 Flash günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Step 3.7 Flash bietet 256K Tokens Kontext gegenüber 128K Tokens bei Command A Plus. Bei der Generierungsgeschwindigkeit liegt Step 3.7 Flash vorn.

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

Beliebige Modelle wechseln & vergleichen

Step 3.7 Flash

Command A Plus

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Step 3.7 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Command A Plus · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Step 3.7 Flash vs Command A Plus: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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