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Command R7B vs Llama 3.3 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Command R7B vs Llama 3.3 70B: Benchmarks, Pricing & Speed Comparison

Command R7B von Cohere und Llama 3.3 70B von Meta gehören zu den führenden KI-Modellen. Command R7B bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $0.07/$0.20 pro 1M Tokens, während Llama 3.3 70B 128K Tokens Kontext zu $0.00/$0.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Cohere

Command R7B

Open-Weights-Basismodell

Command R7B von Cohere ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 140 Token/s Durchsatz, einem Intelligenz-Index von 38/100 und $0.07/$0.2 pro 1M Token.

Intelligence Index38 / 100
Input Token:

$0.07 / 1M

Output Token:

$0.20 / 1M

Context Window:

128K tokens

SWE-bench Verified:

27%

Meta

Llama 3.3 70B

Open-Weights-Basismodell

Llama 3.3 70B von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 280 Token/s Durchsatz, einem Intelligenz-Index von 38/100 und kostenlosem Open-Weights-Zuga

Intelligence Index38 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

Wichtigste Unterschiede auf einen Blick

  • 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 →
FeatureCommand R7BLlama 3.3 70B
Intelligence Index
Composite reasoning & knowledge score (0-100)
3838
Provider
CohereMeta
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 text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
4K4K
Throughput Speed
Tokens generated per second
140 tok/s280 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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

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
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

Interaktiver Token-Kosten-Simulator

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 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

  • 128K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 27% · GPQA Diamond 42.5%)
  • Hohe Generierungsgeschwindigkeit von 140 Token/Sekunde bei nur 290 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (7B) 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

Llama 3.3 70B · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
  • Hohe Generierungsgeschwindigkeit von 280 Token/Sekunde bei nur 250 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (70B) 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

Token Use & Task Economics

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

MetricCommand R7BLlama 3.3 70B
Cost per Task
Estimated USD to finish one task
$0.05$0.21
Output Tokens / Task
Tokens generated per task
20K45K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureCommand R7BLlama 3.3 70B
Official API Model ID (SDK)command-r7bmeta-llama/Llama-3.3-70B
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-122024-12
Knowledge CutoffOctober 2024December 2023
ArchitectureEfficient Compact RAG TransformerDense Autoregressive Transformer
Parameters7B70B
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 ($0.07/1M in) across full 128K windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLCached input at $0.02/1M tokensAutomatic prefix KV-caching in vLLM / SGLang (0 cost)
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) · 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 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 R7B vs Llama 3.3 70B: Schnelle Antworten (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.

Fazit

Sowohl Command R7B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Llama 3.3 70B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Command R7B führt bei komplexem Reasoning und Coding, Llama 3.3 70B liefert den höheren Token-Durchsatz und Command R7B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Command R7B oder Llama 3.3 70B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Command R7B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Command R7B und Llama 3.3 70B?+

Command R7B kostet $0.07 / $0.20 pro 1M Input-/Output-Tokens, Llama 3.3 70B liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Command R7B günstiger.

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

Command R7B bietet 128K Tokens Kontext gegenüber 128K Tokens bei Llama 3.3 70B. Bei der Generierungsgeschwindigkeit liegt Llama 3.3 70B 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 & 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.

Offizielle Quellen: Offizielle API-Dokumentation von Cohere und Meta, Systemkarten und unabhängige Benchmark-Auswertungen (2026).