Head-to-Head Model Comparison

Command R7B vs Llama 3.3 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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Command R7BProfile

Llama 3.3 70BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command R7B

Modello Base a Pesi Aperti

Command R7B 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 140 token/s, indice di intelligenza di 38/100 e t

Intelligence Index38 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.20 / 1M

Context Window:

128K tokens

SWE-bench Verified:

27%

Llama 3.3 70B

Modello Base a Pesi Aperti

Llama 3.3 70B di Meta 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 280 token/s, indice di intelligenza di 38/100 e t

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

Differenze Chiave a Colpo d’Occhio

Llama 3.3 70B is 2.1x cheaper for API usage (input + output tokens combined).
Llama 3.3 70B is faster at 280 tok/s vs 140 tok/s.
Llama 3.3 70B leads on SWE-bench coding (39.5% vs 27.0%).

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

Feature	Command R7B	Llama 3.3 70B
Intelligence Index Composite reasoning & knowledge score (0-100)	38	38
Provider	Cohere	Meta
Input Price Cost per million input tokens	$0.07 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.20 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.05 / 1M	$0.07 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	4K	4K
Throughput Speed Tokens generated per second	140 tok/s	280 tok/s
SWE-bench Verified Real-world software engineering	27%	39.5%
GPQA Diamond Graduate-level reasoning	42.5%	48.9%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.10 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Llama 3.3 70B

+12.5% SWE-bench lead

Cost

Llama 3.3 70B

2.1x cheaper monthly runtime

Speed

Llama 3.3 70B

+100% faster generation

Context

Tied

128K tokens window

Punteggi Benchmark Verificati

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

Command R7BLlama 3.3 70B

SWE-bench Verified (Autonomous Coding)

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

27%vs39.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

42.5%vs48.9%

MMLU-Pro (Multi-task Language Understanding)

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

66%vs71%

LMSYS Chatbot Arena Elo

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

1215ptsvs1285pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs7.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs26.1%

SciCode (Scientific Computing)

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

N/Avs23.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs42.5%

AIME (Advanced Math Competition)

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

N/Avs56.2%

Long-Context Reasoning

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

68%vs63.9%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs280 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 R7B0.29s

Llama 3.3 70B0.25s

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

Command R7B3.86s

Llama 3.3 70B2.04s

Simulatore Interattivo dei Costi Token

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)

Command R7B Monthly API Bill

$0.32 / month

$0.07/1M in · $0.2/1M out

Llama 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

Llama 3.3 70B saves approximately $0.17 / month (52%) under this workload.2.1x Savings

Command R7B · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 4K token (SWE-bench Verified 27% · GPQA Diamond 42.5%)
Throughput elevato di 140 token/secondo con latenza al primo token (TTFT) di soli 290 ms
Pesi aperti (7B) 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

Llama 3.3 70B · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 4K token (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
Throughput elevato di 280 token/secondo con latenza al primo token (TTFT) di soli 250 ms
Pesi aperti (70B) 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.

Metric	Command R7B	Llama 3.3 70B
Cost per Task Estimated USD to finish one task	$0.05	$0.21
Output Tokens / Task Tokens generated per task	20K	45K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Command R7B	Llama 3.3 70B
Official API Model ID (SDK)	command-r7b	meta-llama/Llama-3.3-70B
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-12	2024-12
Knowledge Cutoff	October 2024	December 2023
Architecture	Efficient Compact RAG Transformer	Dense Autoregressive Transformer
Parameters	7B	70B
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)	Flat rate ($0.07/1M in) across full 128K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.02/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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
Output Modalities	text	text

Command R7B vs Llama 3.3 70B: Risposte Rapide (FAQ)

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 140 tokens per second.

Which model is cheaper to run?

Command R7B is cheaper at $0.05 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.29s.

Verdetto

Sia Command R7B (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 Llama 3.3 70B (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 R7B guida nei benchmark di ragionamento e programmazione, Llama 3.3 70B garantisce una generazione più rapida e Command R7B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Command R7B o Llama 3.3 70B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Command R7B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Command R7B e Llama 3.3 70B?+

Command R7B costa $0.07 / $0.20 per milione di token di input/output, mentre Llama 3.3 70B costa $0.00 / $0.00. Nel costo combinato, Command R7B risulta più economico.

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

Command R7B supporta 128K token di contesto contro 128K token di Llama 3.3 70B, mentre Llama 3.3 70B 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Command R7B vs Llama 3.3 70B

Cambia e Confronta Qualsiasi Modello

Command R7B

Llama 3.3 70B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Command R7B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Command R7B vs Llama 3.3 70B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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