Command R7B vs Llama 3.3 70B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command R7B by Cohere and Llama 3.3 70B by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Llama 3.3 70B holds a notable pricing advantage for token ingestion. On output generation, Llama 3.3 70B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Llama 3.3 70B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Command R7B
Открытые веса (Open Weights)Command R7B от Cohere — передовая модель искусственного интеллекта с контекстным окном 128K токенов и стоимостью API $0.07/1M входящих и $0.2/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.07 / 1M
$0.20 / 1M
128K tokens
27%
Llama 3.3 70B
Открытые веса (Open Weights)Llama 3.3 70B от Meta — передовая модель искусственного интеллекта с контекстным окном 128K токенов и стоимостью API $0.0/1M входящих и $0.0/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.00 / 1M
$0.00 / 1M
128K tokens
39.5%
Ключевые отличия кратко
- 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%).
| 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 |
Кто лидирует в каждой категории?
At-a-glance decision frameworkРезультаты проверенных бенчмарков
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Задержка и скорость ответа
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Интерактивный калькулятор стоимости токенов
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Command R7B · Strengths & Key Advantages
- Современная архитектура Efficient Compact RAG Transformer от Cohere с поддержкой до 128K токенов контекста
- Оптимальная стоимость использования ($0.07/1M вход, $0.2/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Llama 3.3 70B · Strengths & Key Advantages
- Современная архитектура Dense Autoregressive Transformer от Meta с поддержкой до 128K токенов контекста
- Оптимальная стоимость использования ($0.0/1M вход, $0.0/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
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 |
Технические характеристики
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: Быстрые ответы (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.
Вердикт
Verdict & Model Selection Recommendation: Choose Command R7B if your priority is Edge RAG applications, local legal search, and budget-constrained API pipelines. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.3 70B if your workflow prioritizes Private on-premises deployments, high-throughput text summarization, and fine-tuned domain tasks. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Часто задаваемые вопросы (FAQ)
How does Command R7B compare to Llama 3.3 70B in API token pricing and cost per token?+
Command R7B costs $0.07 per 1M input tokens and $0.20 per 1M output tokens, whereas Llama 3.3 70B costs $0.00 input and $0.00 output. Overall, Llama 3.3 70B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Command R7B or Llama 3.3 70B?+
Both Command R7B and Llama 3.3 70B offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Command R7B or Llama 3.3 70B?+
Command R7B scores 27% on SWE-bench Verified while Llama 3.3 70B scores 39.5%. For autonomous software engineering and multi-file debugging, Llama 3.3 70B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Command R7B and Llama 3.3 70B?+
Command R7B is optimized for low-latency direct generation. In contrast, Llama 3.3 70B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Command R7B and Llama 3.3 70B?+
Command R7B generates approximately 140 tokens per second, while Llama 3.3 70B delivers 280 tokens per second. In latency-sensitive production environments, Llama 3.3 70B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Command R7B and Llama 3.3 70B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.3 70B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Command R7B. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
Официальные источники: GetInfoUs Independent AI Benchmarks & Official API Specs