Head-to-Head Model Comparison

Command A Reasoning vs GPT-6.1 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

تمت المراجعة بواسطة فريق أبحاث GetInfoUsآخر مراجعة: October 2026السياسة التحريرية
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Compare Command A Reasoning by Cohere against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.

مقارنة مباشرة (VS)

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Cohere

Command A Reasoning

نموذج مفتوح الأوزان

نموذج Command A Reasoning المقدم من Cohere يوفر نافذة سياق تصل إلى 256K رمز، بتكلفة $2.5 لكل مليون رمز إدخال و$10 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.

Intelligence Index55 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

53%

OpenAI

GPT-6.1 Sol

نموذج استدلال عميق

نموذج GPT-6.1 Sol المقدم من OpenAI يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $2.0 لكل مليون رمز إدخال و$10.0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.

Intelligence Index94 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.3%

أهم الفروقات في لمحة

  • GPT-6.1 Sol is 1.1x cheaper for API usage (input + output tokens combined).
  • GPT-6.1 Sol has a larger context window (1M vs 256K tokens).
  • GPT-6.1 Sol is faster at 120 tok/s vs 55 tok/s.
  • GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 53.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureCommand A ReasoningGPT-6.1 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
5594
Provider
CohereOpenAI
Input Price
Cost per million input tokens
$2.50 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.02 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
256K tokens~384 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
33K128K
Throughput Speed
Tokens generated per second
55 tok/s120 tok/s
SWE-bench Verified
Real-world software engineering
53%82.3%
GPQA Diamond
Graduate-level reasoning
70%80.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.10 / 1M

من يتفوق في كل مجال؟

At-a-glance decision framework
Coding
GPT-6.1 Sol
+29.3% SWE-bench lead
Cost
GPT-6.1 Sol
1.1x cheaper monthly runtime
Speed
GPT-6.1 Sol
+118% faster generation
Context
GPT-6.1 Sol
1M tokens window

نتائج الاختبارات المعيارية الموثقة

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

Command A ReasoningGPT-6.1 Sol
SWE-bench Verified (Autonomous Coding)

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

53%vs82.3%
GPQA Diamond (Doctorate-Level Science & Math)

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

70%vs80.8%
MMLU-Pro (Multi-task Language Understanding)

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

84.5%vs89.9%
LMSYS Chatbot Arena Elo

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

1370ptsvs1519pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs18.1%
Terminal-Bench (Agentic Coding & Terminal Use)

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

47%vs63.6%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs54.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs79.7%
AIME (Advanced Math Competition)

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

78%vs93.5%
Long-Context Reasoning

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

86%vs91%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

55 tok/s vs120 tok/s

زمن الاستجابة والسرعة

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
Command A Reasoning0.75s
GPT-6.1 Sol1.12s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command A Reasoning9.84s
GPT-6.1 Sol5.29s

محاكي تكلفة الرموز التفاعلي

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Command A Reasoning Monthly API Bill
$13.88 / month
$2.5/1M in · $10/1M out
GPT-6.1 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
GPT-6.1 Sol saves approximately $1.72 / month (12%) under this workload.1.1x Savings

Command A Reasoning · Strengths & Key Advantages

  • بنية Enterprise Chain-of-Thought Reasoning Transformer متطورة من Cohere مع سعة سياق تبلغ 256K رمز
  • كفاءة عالية في التكلفة ($2.5/1M إدخال و$10/1M إخراج) وسرعة استجابة ممتازة
  • أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
  • يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
  • قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة

GPT-6.1 Sol · Strengths & Key Advantages

  • بنية Near-Astra unified reasoning model متطورة من OpenAI مع سعة سياق تبلغ 1M رمز
  • كفاءة عالية في التكلفة ($2.0/1M إدخال و$10.0/1M إخراج) وسرعة استجابة ممتازة
  • أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
  • يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
  • قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricCommand A ReasoningGPT-6.1 Sol
Cost per Task
Estimated USD to finish one task
$0.95$2.05
Output Tokens / Task
Tokens generated per task
45K62K
Reasoning Tokens / Task
Thinking tokens burned per task
22K36K

المواصفات التقنية

Architecture, release timeline, capabilities, and modalities.

FeatureCommand A ReasoningGPT-6.1 Sol
Official API Model ID (SDK)command-a-reasoninggpt-6-1-sol
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2025-082026-09
Knowledge CutoffJune 2025April 2026
ArchitectureEnterprise Chain-of-Thought Reasoning TransformerNear-Astra unified reasoning model
ParametersConfidential (~100B+)Confidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($2.5/1M in) across full 256K windowFlat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.75/1M tokensAutomatic prefix caching at $0.1/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 62 GB VRAM (4-bit Quantized) · 215 GB VRAM (Native BF16)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpen-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 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext, vision
Output Modalitiestexttext

Command A Reasoning vs GPT-6.1 Sol: إجابات سريعة (FAQ)

Which is more intelligent, Command A Reasoning or GPT-6.1 Sol?

GPT-6.1 Sol is more intelligent. It scores 94 compared with 55 on the composite Intelligence Index.

Which model is faster?

GPT-6.1 Sol is faster, generating 120 tokens per second compared with 55 tokens per second.

Which model is cheaper to run?

GPT-6.1 Sol is cheaper at $1.47 per 1M tokens blended, compared with $2.02 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command A Reasoning has lower latency, with a time to first token of 0.75s 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 256K tokens.

الحكم النهائي

Choose Command A Reasoning for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات cohere الإنتاجية, or choose GPT-6.1 Sol for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات openai الإنتاجية.

الأسئلة الشائعة (FAQ)

Which is cheaper: Command A Reasoning or GPT-6.1 Sol?+

Command A Reasoning costs $2.5/1M input and $10/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: Command A Reasoning or GPT-6.1 Sol?+

Command A Reasoning supports 256K tokens whereas GPT-6.1 Sol supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

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

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

المصادر الرسمية: Cohere & OpenAI Official Documentation