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Head-to-Head Model Comparison

Command R+ vs Command R

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Command R+ vs Command R: Benchmarks, Pricing & Speed Comparison

Command R+ di Cohere e Command R di Cohere sono due modelli di IA di riferimento. Command R+ offre una finestra di contesto di 128K token a $2.50/$10.00 per milione di token, mentre Command R mette a disposizione 128K token di contesto a $0.15/$0.60 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

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Cohere

Command R+

Modello Base a Pesi Aperti

Command R+ di Cohere è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 65 token/s, indice di intelligenza di 48/100 e tar

Intelligence Index48 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

41.5%

Cohere

Command R

Modello Base a Pesi Aperti

Command R di Cohere è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 85 token/s, indice di intelligenza di 43/100 e tari

Intelligence Index43 / 100
Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

32%

Differenze Chiave a Colpo d’Occhio

  • Command R is 16.5x cheaper for API usage (input + output tokens combined).
  • Command R is faster at 85 tok/s vs 65 tok/s.
  • Command R+ leads on SWE-bench coding (41.5% vs 32.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureCommand R+Command R
Intelligence Index
Composite reasoning & knowledge score (0-100)
4843
Provider
CohereCohere
Input Price
Cost per million input tokens
$2.50 / 1M$0.15 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$0.60 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.02 / 1M$0.13 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K4K
Throughput Speed
Tokens generated per second
65 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
41.5%32%
GPQA Diamond
Graduate-level reasoning
56.5%46%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.05 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
Command R+
+9.5% SWE-bench lead
Cost
Command R
16.5x cheaper monthly runtime
Speed
Command R
+31% faster generation
Context
Tied
128K tokens window

Punteggi Benchmark Verificati

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

Command R+Command R
SWE-bench Verified (Autonomous Coding)

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

41.5%vs32%
GPQA Diamond (Doctorate-Level Science & Math)

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

56.5%vs46%
MMLU-Pro (Multi-task Language Understanding)

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

78.5%vs71%
LMSYS Chatbot Arena Elo

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

1300ptsvs1240pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

36%vs28%
AIME (Advanced Math Competition)

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

66%vs52%
Long-Context Reasoning

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

80%vs75%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs85 tok/s

Latenza e Tempo di Risposta

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 R+0.58s
Command R0.42s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command R+8.27s
Command R6.30s

Simulatore Interattivo dei Costi Token

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
$13.88 / month
$2.5/1M in · $10/1M out
Command R Monthly API Bill
$0.84 / month
$0.15/1M in · $0.6/1M out
Command R saves approximately $13.04 / month (94%) under this workload.16.5x Savings

Command R+ · Strengths & Key Advantages

  • Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 41.5% · GPQA Diamond 56.5%)
  • Throughput elevato di 65 token/secondo con latenza al primo token (TTFT) di soli 580 ms
  • Pesi aperti (104B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
  • Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
  • Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Command R · Strengths & Key Advantages

  • Finestra di contesto da 128K con capacità massima di output fino a 4K token (SWE-bench Verified 32% · GPQA Diamond 46%)
  • Throughput elevato di 85 token/secondo con latenza al primo token (TTFT) di soli 420 ms
  • Pesi aperti (35B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
  • Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
  • Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Token Use & Task Economics

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

MetricCommand R+Command R
Cost per Task
Estimated USD to finish one task
$0.65$0.12
Output Tokens / Task
Tokens generated per task
35K25K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureCommand R+Command R
Official API Model ID (SDK)command-r-pluscommand-r
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-082024-08
Knowledge CutoffJune 2024June 2024
ArchitectureEnterprise-Grade Dense TransformerRetrieval-Augmented Dense Transformer
Parameters104B35B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($2.5/1M in) across full 128K windowFlat rate ($0.15/1M in) across full 128K window
Prompt Cache Policy & TTLCached input at $0.75/1M tokensCached input at $0.05/1M tokens
Computer Use / GUI AgentAPI / 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 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 toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 64 GB VRAM (4-bit Quantized) · 224 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 22 GB VRAM (4-bit Quantized) · 75 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Command R+ vs Command R: Risposte Rapide (FAQ)

Which is more intelligent, Command R+ or Command R?

Command R+ is more intelligent. It scores 48 compared with 43 on the composite Intelligence Index.

Which model is faster?

Command R is faster, generating 85 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Command R is cheaper at $0.13 per 1M tokens blended, compared with $2.02 (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 0.58s.

Verdetto

Sia Command R+ (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Command R (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Command R+ guida nei benchmark di ragionamento e programmazione, Command R garantisce una generazione più rapida e Command R offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Command R+ o Command R?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Command R+. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Command R+ e Command R?+

Command R+ costa $2.50 / $10.00 per milione di token di input/output, mentre Command R costa $0.15 / $0.60. Nel costo combinato, Command R risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Command R+ supporta 128K token di contesto contro 128K token di Command R, mentre Command R offre una velocità di generazione superiore.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Fonti ufficiali: Documentazione API ufficiale di Cohere e Cohere, schede di sistema e benchmark indipendenti (2026).