Command R (08-2024) vs Mistral Small 4
| Signal | Command R (08-2024) | Delta | Mistral Small 4 |
|---|---|---|---|
Capabilities | 50 | -33 | |
Benchmarks | 38 | +38 | |
Pricing | 99 | -- | |
Context window size | 81 | -5 | |
Recency | 0 | -90 | |
Output Capacity | 58 | -27 | |
| Overall Result | 1 wins | of 6 | 4 wins |
Score History
40.5
current score
Command R (08-2024)
right now
40
current score
Command R (08-2024)
Cohere
Mistral Small 4
Mistral AI
| Metric | Command R (08-2024) | Mistral Small 4 | Winner |
|---|---|---|---|
| Overall Score | 41 | 40 | Command R (08-2024) |
| Rank | #173 | #343 | Command R (08-2024) |
| Quality Rank | #173 | #343 | Command R (08-2024) |
| Adoption Rank | #173 | #343 | Command R (08-2024) |
| Parameters | -- | -- | -- |
| Context Window | 128K | 262K | Mistral Small 4 |
| Pricing | $0.15/$0.60/M | $0.15/$0.60/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 83 | Mistral Small 4 |
| Benchmarks | 38 | -- | Command R (08-2024) |
| Pricing | 99 | 99 | Command R (08-2024) |
| Context window size | 81 | 86 | Mistral Small 4 |
| Recency | 0 | 90 | Mistral Small 4 |
| Output Capacity | 58 | 85 | Mistral Small 4 |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 41/100 (rank #173), placing it in the top 41% of all 290 models tracked.
Scores 40/100 (rank #343), placing it in the top -18% of all 290 models tracked.
With only a 1-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Choose Command R (08-2024) when you need:
- Budget-friendly applications with moderate quality requirements
Choose Mistral Small 4 when you need:
- Processing long documents or large codebases (262K token context)
- Multimodal workflows that require image understanding
- Step-by-step reasoning and chain-of-thought problem solving
- Self-hosted deployments where you need full control over the model
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Command R (08-2024) also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.60/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (41/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Command R (08-2024) and Mistral Small 4 are extremely close in overall performance (only 0.5 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
By Use Case
Best for Quality
Command R (08-2024)
Marginally better benchmark scores; both are excellent
Best for Cost
Command R (08-2024)
0% lower pricing; better value at scale
Best for Reliability
Command R (08-2024)
Higher uptime and faster response speeds
Best for Prototyping
Command R (08-2024)
Stronger community support and better developer experience
Best for Production
Command R (08-2024)
Wider enterprise adoption and proven at scale
by Cohere
- Choose for Quality - Marginally better benchmark scores; both are excellent
- Choose for Cost - 0% lower pricing; better value at scale
- Choose for Reliability - Higher uptime and faster response speeds
- Choose for Prototyping - Stronger community support and better developer experience
- Choose for Production - Wider enterprise adoption and proven at scale
| Capability | Command R (08-2024) | Mistral Small 4 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Command R (08-2024)
Cohere
Mistral Small 4
Mistral AI
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Command R (08-2024) | Mistral Small 4 |
|---|---|---|
| Context Window | 128K | 262K |
| Max Output Tokens | 4,000 | 209,715 |
| Open Source | No | Yes |
| Created | Aug 30, 2024 | Mar 16, 2026 |