Google (29 models) vs Cohere (4 models) - compared across composite scores, pricing, capabilities, and context windows.
| Score | vs | Cohere | Score | |
|---|---|---|---|---|
| Gemini 3 Flash Preview | 88 | Command A | 51 | |
| Gemini 2.5 Pro | 84 | Command R+ (08-2024) | 49 | |
| Gemini 2.5 Pro Preview 06-05 | 84 | Command R7B (12-2024) | 36 |
| Capability | Cohere | Leader | |
|---|---|---|---|
Vision | 25/29 | 0/4 | |
Reasoning | 17/29 | 0/4 | |
Function Calling | 19/29 | 2/4 | |
JSON Mode | 26/29 | 4/4 | |
Web Search | 16/29 | 0/4 | |
Streaming | 27/29 | 4/4 | |
Image Output | 4/29 | 0/4 |
| Metric | Cohere | |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.040 Gemma 3 4B | $0.037 Command R7B (12-2024) |
| Cheapest Output (per 1M tokens) | $0.080 | $0.150 |
| Most Expensive Input (per 1M tokens) | $2.00 Gemini 3.1 Pro Preview Custom Tools | $2.50 Command A |
| Most Expensive Output (per 1M tokens) | $12.00 | $10.00 |
| Free Models | 4 | 0 |
| Max Context Window | 1.0M | 256K |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Gemini 3 Flash Preview | 88 | $0.500 | $3.00 |
| Gemini 2.5 Pro | 84 | $1.25 | $10.00 |
| Gemini 2.5 Pro Preview 06-05 | 84 | $1.25 | $10.00 |
| Gemini 2.5 Pro Preview 05-06 | 84 | $1.25 | $10.00 |
| Gemini 3.1 Pro Preview Custom Tools | 81 | $2.00 | $12.00 |
| Gemini 3.1 Pro Preview | 81 | $2.00 | $12.00 |
| Gemma 4 31B (free) | 81 | Free | Free |
| Gemma 4 31B | 81 | $0.130 | $0.380 |
| Gemini 3.1 Flash Lite Preview | 80 | $0.250 | $1.50 |
| Gemini 2.5 Flash Lite Preview 09-2025 | 79 | $0.100 | $0.400 |
| Gemini 2.5 Flash Lite | 79 | $0.100 | $0.400 |
| Gemini 2.5 Flash | 79 | $0.300 | $2.50 |
| Gemma 2 27B | 77 | $0.650 | $0.650 |
| Gemma 4 26B A4B (free) | 73 | Free | Free |
| Gemma 4 26B A4B | 73 | $0.060 | $0.330 |
| Gemini 2.0 Flash | 72 | $0.100 | $0.400 |
| Gemini 2.0 Flash Lite | 59 | $0.075 | $0.300 |
| Lyria 3 Pro Preview | 40 | Free | Free |
| Lyria 3 Clip Preview | 40 | Free | Free |
| Gemma 3n 4B | 40 | $0.060 | $0.120 |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Command A | 51 | $2.50 | $10.00 |
| Command R+ (08-2024) | 49 | $2.50 | $10.00 |
| Command R (08-2024) | 49 | $0.150 | $0.600 |
| Command R7B (12-2024) | 36 | $0.037 | $0.150 |
Compare any two AI providers side-by-side.
Google's strategy emphasizes accessibility and experimentation, offering everything from free lightweight models to Gemini 2.5 Flash Lite Preview (60/100 score) at $0.040/M output tokens. Cohere targets enterprise customers exclusively with 4 models priced between $0.150-$10.00/M, sacrificing breadth for a focused premium offering that averages 36/100 in benchmarks.
Google dominates multimodal use cases with 79% of its portfolio supporting vision and 47% supporting reasoning, compared to Cohere's text-only approach. For applications requiring image understanding or visual reasoning, Google's extensive vision coverage makes Cohere a non-starter despite Command R+ scoring a respectable 38/100 on text tasks.
Google's 4x larger context window (1M vs 256K) enables processing entire codebases or lengthy documents that would require chunking with Cohere. While Cohere's Command R+ at 256K context still handles most RAG scenarios adequately, Google's million-token capacity better suits applications analyzing multiple large documents simultaneously or maintaining extensive conversation histories.
Google leverages its 15 open-source models and massive scale to offer budget options like Gemini Flash variants starting at $0.040/M tokens. Cohere's minimum $0.150/M pricing reflects their enterprise-only positioning with no free tier, betting that their specialized retrieval capabilities in Command R models justify premium pricing over Google's volume approach.
Cohere's Command R+ excels specifically at retrieval-augmented generation with built-in citation capabilities, making it ideal for enterprise search and knowledge base applications despite scoring 22 points lower overall. Google's models score higher on general benchmarks but lack Cohere's specialized RAG optimizations, requiring more custom implementation for citation-heavy enterprise use cases.