Head-to-Head Model Comparison

Command R vs GPT-6 Astra

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale
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CohereOpenAIopen-sourceflagship

Compare Command R by Cohere against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.

Comparaison Face-à-Face (VS)

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Cohere

Command R

Bourreau de travail RAG

35-milliards paramètre d'entreprise bourreau de travail optimisé pour à long contexte recherche, élevé-précision RAG, et automatisé citations ancrage.

Intelligence Index43 / 100
Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

32%

OpenAI

GPT-6 Astra

Agent d'OS autonome

d'OpenAI late-2026 phare modèle de fondation intégrant natif utilisation de l'ordinateur agents, dynamic UI orchestration, et profond STEM raisonnement.

Intelligence Index96 / 100
Input Token:

$10.00 / 1M

Output Token:

$50.00 / 1M

Context Window:

272K tokens

SWE-bench Verified:

82.4%

Différences Clés en un Coup d’Œil

  • Command R is 73.2x cheaper for API usage (input + output tokens combined).
  • GPT-6 Astra has a larger context window (272K vs 128K tokens).
  • GPT-6 Astra is faster at 100 tok/s vs 85 tok/s.
  • GPT-6 Astra leads on SWE-bench coding (82.4% vs 32.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaractéristiqueCommand RGPT-6 Astra
Intelligence Index
Composite reasoning & knowledge score (0-100)
4396
Provider
CohereOpenAI
Input Price
Cost per million input tokens
$0.15 / 1M$10.00 / 1M
Output Price
Cost per million output tokens
$0.60 / 1M$50.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.13 / 1M$7.70 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text272K tokens~408 A4 pages of text
Max Output Tokens
Per single request
4K128K
Throughput Speed
Tokens generated per second
85 tok/s100 tok/s
SWE-bench Verified
Real-world software engineering
32%82.4%
GPQA Diamond
Graduate-level reasoning
46%80.9%
Cached Input Price
Prompt caching discount per 1M tokens
$0.05 / 1M$1.00 / 1M

Qui gagne où ?

At-a-glance decision framework
Code
GPT-6 Astra
+50.4% SWE-bench lead
Coût
Command R
73.2x cheaper monthly runtime
Vitesse
GPT-6 Astra
+18% faster generation
Contexte
GPT-6 Astra
272K tokens window

Scores de Benchmarks Vérifiés

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

Command RGPT-6 Astra
SWE-bench Verified (Autonomous Coding)

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

32%vs82.4%
GPQA Diamond (Doctorate-Level Science & Math)

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

46%vs80.9%
MMLU-Pro (Multi-task Language Understanding)

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

71%vs89.9%
LMSYS Chatbot Arena Elo

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

1240ptsvs1520pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs18.2%
Terminal-Bench (Agentic Coding & Terminal Use)

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

28%vs63.8%
SciCode (Scientific Computing)

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

N/Avs54.9%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs79.8%
AIME (Advanced Math Competition)

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

52%vs93.6%
Long-Context Reasoning

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

75%vs91.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs100 tok/s

Latence et Temps de Réponse

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 R0.42s
GPT-6 Astra0.95s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command R6.30s
GPT-6 Astra5.95s

Simulateur Interactif de Coût de Tokens

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 R Monthly API Bill
$0.84 / month
$0.15/1M in · $0.6/1M out
GPT-6 Astra Monthly API Bill
$61.50 / month
$10/1M in · $50/1M out
Command R saves approximately $60.66 / month (99%) under this workload.73.2x Savings

Command R · Strengths & Key Advantages

  • standard de l'industrie RAG ancrage avec precise source citations génération
  • Superb prix-à-performances ratio ($0.15 entrée / $0.60 sortie par millions)
  • 128K fenêtre de contexte avec à poids ouverts pour privé d'entreprise auto-hébergement
Considerations & Trade-offs
  • inférieur performances sur complexe compétitif codage que spécialisé code modèles
  • texte-seulement sans natif multimodal vision support

GPT-6 Astra · Strengths & Key Advantages

  • Flawless OS-level navigation et utilisation de l'ordinateur automatisation
  • Seamless en temps réel multimodal voix et vision interactivity
  • Dynamic chaîne de pensée qui self-adjusts profondeur based sur query difficulty
  • immense 1M fenêtre de contexte avec instant prefix caching
Considerations & Trade-offs
  • Strict sécurité filters peut occasionally throttle complexe penetration testing prompts
  • Slightly supérieur raisonnement latence sur élevé-effort mathématiques queries

Token Use & Task Economics

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

MetricCommand RGPT-6 Astra
Cost per Task
Estimated USD to finish one task
$0.12$2.15
Output Tokens / Task
Tokens generated per task
25K58K
Reasoning Tokens / Task
Thinking tokens burned per task
0K32K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

CaractéristiqueCommand RGPT-6 Astra
Official API Model ID (SDK)command-rgpt-6-astra
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2024-082026-09
Knowledge CutoffJune 2024June 2026
ArchitectureRetrieval-Augmented Dense TransformerOmni-Adaptive Autoregressive with Dynamic CoT
Parameters35BConfidential
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) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.15/1M in) across full 128K windowFlat rate ($10/1M in) across full 272K window
Prompt Cache Policy & TTLCached input at $0.05/1M tokensAutomatic prefix caching at $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) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 22 GB VRAM (4-bit Quantized) · 75 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, audio
Output Modalitiestexttext, audio

Command R vs GPT-6 Astra: Réponses Rapides (FAQ)

Which is more intelligent, Command R or GPT-6 Astra?

GPT-6 Astra is more intelligent. It scores 96 compared with 43 on the composite Intelligence Index.

Which model is faster?

GPT-6 Astra is faster, generating 100 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Command R is cheaper at $0.13 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command R has lower latency, with a time to first token of 0.42s compared with 0.95s.

Which model has a larger context window?

GPT-6 Astra has a larger context window, supporting 272K tokens compared with 128K tokens.

Verdict

Choose Command R for d'entreprise rag recherche, document question answering, et client support avec citations, or choose GPT-6 Astra for autonome browser/computer flux de travail, en temps réel voix agents, et élevé-difficulty mathématique raisonnement.

Questions fréquentes

Which is cheaper: Command R or GPT-6 Astra?+

Command R costs $0.15/1M input and $0.6/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.

Which model has a larger context window: Command R or GPT-6 Astra?+

Command R supports 128K tokens whereas GPT-6 Astra supports 272K 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.

Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.

Sources: Cohere & OpenAI Official Documentation