| Signal | Command R7B (12-2024) | Delta | GPT-3.5 Turbo 16k |
|---|---|---|---|
Capabilities | 33 | -17 | |
Benchmarks | 38 | +38 | |
Pricing | 0 | -4 | |
Context window size | 81 | +14 | |
Recency | 48 | +48 | |
Output Capacity | 60 | 0 | |
| Overall Result | 3 wins | of 6 | 3 wins |
27
days ranked higher
1
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Cohere
OpenAI
Command R7B (12-2024) saves you $488.75/month
That's $5865.00/year compared to GPT-3.5 Turbo 16k at your current usage level of 100K calls/month.
| Metric | Command R7B (12-2024) | GPT-3.5 Turbo 16k | Winner |
|---|---|---|---|
| Overall Score | 45 | 40 | Command R7B (12-2024) |
| Rank | #271 | #283 | Command R7B (12-2024) |
| Quality Rank | #271 | #283 | Command R7B (12-2024) |
| Adoption Rank | #271 | #283 | Command R7B (12-2024) |
| Parameters | 7B | -- | -- |
| Context Window | 128K | 16K | Command R7B (12-2024) |
| Pricing | $0.04/$0.15/M | $3.00/$4.00/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 50 | GPT-3.5 Turbo 16k |
| Benchmarks | 38 | -- | Command R7B (12-2024) |
| Pricing | 0 | 4 | GPT-3.5 Turbo 16k |
| Context window size | 81 | 67 | Command R7B (12-2024) |
| Recency | 48 | 0 | Command R7B (12-2024) |
| Output Capacity | 60 | 60 | GPT-3.5 Turbo 16k |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 45/100 (rank #271), placing it in the top 7% of all 290 models tracked.
Scores 40/100 (rank #283), placing it in the top 3% of all 290 models tracked.
With only a 5-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.
Command R7B (12-2024) offers 97% better value per quality point. At 1M tokens/day, you'd spend $2.81/month with Command R7B (12-2024) vs $105.00/month with GPT-3.5 Turbo 16k - a $102.19 monthly difference.
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
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Command R7B (12-2024) also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (128K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.15/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (45/100) correlates with better nuance, coherence, and style in long-form content
Command R7B (12-2024) has a moderate advantage with a 4.700000000000003-point lead in composite score. It wins on more signal dimensions, but GPT-3.5 Turbo 16k has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Command R7B (12-2024)
Marginally better benchmark scores; both are excellent
Best for Cost
Command R7B (12-2024)
97% lower pricing; better value at scale
Best for Reliability
Command R7B (12-2024)
Higher uptime and faster response speeds
Best for Prototyping
Command R7B (12-2024)
Stronger community support and better developer experience
Best for Production
Command R7B (12-2024)
Wider enterprise adoption and proven at scale
by Cohere
| Capability | Command R7B (12-2024) | GPT-3.5 Turbo 16k |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Cohere
OpenAI
Command R7B (12-2024) saves you $9.95/month
That's 98% cheaper than GPT-3.5 Turbo 16k at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Command R7B (12-2024) | GPT-3.5 Turbo 16k |
|---|---|---|
| Context Window | 128K | 16K |
| Max Output Tokens | 4,000 | 4,096 |
| Open Source | No | No |
| Created | Dec 14, 2024 | Aug 28, 2023 |
Command R7B (12-2024) scores 45/100 (rank #271) compared to GPT-3.5 Turbo 16k's 40/100 (rank #283), giving it a 5-point advantage. Command R7B (12-2024) is the stronger overall choice, though GPT-3.5 Turbo 16k may excel in specific areas like certain benchmarks.
Command R7B (12-2024) is ranked #271 and GPT-3.5 Turbo 16k is ranked #283 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Command R7B (12-2024) is cheaper at $0.15/M output tokens vs GPT-3.5 Turbo 16k's $4.00/M output tokens - 26.7x more expensive. Input token pricing: Command R7B (12-2024) at $0.04/M vs GPT-3.5 Turbo 16k at $3.00/M.
Command R7B (12-2024) has a larger context window of 128,000 tokens compared to GPT-3.5 Turbo 16k's 16,385 tokens. A larger context window means the model can process longer documents and conversations.