Command-R (Mar '24)
Command-R (Mar '24) is an API model from Cohere. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.
Intelligence
Below avg
Cost
Moderate
$0.50 in / $1.50 out
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Context Window
128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
Availability
API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Smaller / cheaper Command R — tier below Command R+.
- Industry-leading RAG with in-line citations baked into the response — Cohere's signature differentiator
- 104B params, 88.2% MMLU at release — competitive with GPT-4 Turbo on enterprise tasks
- Beats Claude 3 Sonnet, Mistral Large, and GPT-4 Turbo on Cohere's internal RAG / tool-use benchmarks
Best use cases
- Enterprise RAG where citation accuracy and hallucination reduction matter most
- Multi-step tool-use agents in regulated industries
- Multilingual customer-facing assistants beyond English
Tools to try
Not ideal for
- Frontier-leaderboard reasoning by 2025 — newer Cohere Command-A and competitors lead
- Consumer / chatbot use cases where personality and creativity matter