Command R7B vs Aya Expanse 32B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Command R7B by Cohere and Aya Expanse 32B by Cohere 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, Command R7B holds a notable pricing advantage for token ingestion. On output generation, Command R7B 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, Command R7B 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
Small RAGLightweight 7B parameter retrieval engine bringing Cohere citation grounding and 128K context to single-GPU edge servers.
$0.07 / 1M
$0.20 / 1M
128K tokens
27%
Aya Expanse 32B
Multilingual 32BHigh-capacity 32B multilingual model delivering superior performance across 23 global languages with rich nuance and 128K context.
$0.25 / 1M
$0.50 / 1M
128K tokens
33.5%
Key Differences at a Glance
- Command R7B is 2.9x cheaper for API usage (input + output tokens combined).
- Command R7B is faster at 140 tok/s vs 85 tok/s.
- Aya Expanse 32B leads on SWE-bench coding (33.5% vs 27.0%).
| Feature | Command R7B | Aya Expanse 32B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 44 |
Provider | Cohere | Cohere |
Input Price Cost per million input tokens | $0.07 / 1M | $0.25 / 1M |
Output Price Cost per million output tokens | $0.20 / 1M | $0.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $0.14 / 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 | 8K |
Throughput Speed Tokens generated per second | 140 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 27% | 33.5% |
GPQA Diamond Graduate-level reasoning | 42.5% | 50% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.06 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Command R7B · Strengths & Key Advantages
- Remarkable 128K context RAG performance in an ultra-compact 7B size
- High inference velocity of 140 tokens/sec on single GPU cards
- Built-in verifiable citation extraction and tool use
Considerations & Trade-offs
- Reduced reasoning capacity compared to Command R and R+
- Text-only without native multimodal capabilities
Aya Expanse 32B · Strengths & Key Advantages
- Substantially higher reasoning and factual accuracy in non-English languages
- Generous 128K context window for long multilingual document translation
- Open weights under permissive license for global researchers and enterprises
Considerations & Trade-offs
- Slightly lower coding benchmarks than English-centric 32B peers
- Requires substantial GPU VRAM for multi-turn local serving
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Command R7B | Aya Expanse 32B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $0.18 |
Output Tokens / Task Tokens generated per task | 20K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Command R7B | Aya Expanse 32B |
|---|---|---|
| Official API Model ID (SDK) | command-r7b | aya-expanse-32b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-12 | 2024-10 |
| Knowledge Cutoff | October 2024 | August 2024 |
| Architecture | Efficient Compact RAG Transformer | High-Capacity Multilingual Transformer |
| Parameters | 7B | 32B |
| 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 | Flat rate ($0.25/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Cached input at $0.06/1M tokens |
| 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) · 20 GB VRAM (4-bit Quantized) · 69 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 Aya Expanse 32B: Quick Answers
Which is more intelligent, Command R7B or Aya Expanse 32B?
Aya Expanse 32B is more intelligent. It scores 44 compared with 38 on the composite Intelligence Index.
Which model is faster?
Command R7B is faster, generating 140 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Command R7B is cheaper at $0.05 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Command R7B has lower latency, with a time to first token of 0.29s compared with 0.44s.
Verdict
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 Aya Expanse 32B if your workflow prioritizes Global enterprise communications, international legal translation, and academic research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Command R7B compare to Aya Expanse 32B 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 Aya Expanse 32B costs $0.25 input and $0.50 output. Overall, Command R7B 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 Aya Expanse 32B?+
Both Command R7B and Aya Expanse 32B 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 Aya Expanse 32B?+
Command R7B scores 27% on SWE-bench Verified while Aya Expanse 32B scores 33.5%. For autonomous software engineering and multi-file debugging, Aya Expanse 32B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Command R7B and Aya Expanse 32B?+
Command R7B is optimized for low-latency direct generation. In contrast, Aya Expanse 32B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Command R7B and Aya Expanse 32B?+
Command R7B generates approximately 140 tokens per second, while Aya Expanse 32B delivers 85 tokens per second. In latency-sensitive production environments, Command R7B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Command R7B and Aya Expanse 32B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Command R7B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Aya Expanse 32B. 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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs