Aya Vision 32B vs Gemini 2.5 Computer Use Preview vs o1-pro
o1-pro comes out ahead, 55 to 46 and 15 on our weighted score, though Gemini 2.5 Computer Use Preview is 76× cheaper per token.
CohereAya Vision 32B
15/100- ECI—
- Price—
- Context16K
GoogleGemini 2.5 Computer Use Preview
46/100- ECI—
- Price$1.25 / $10.00
- Context128K
- Our pick
OpenAIo1-pro
55/100- ECI—
- Price$150.00 / $600.00
- Context200K
o1-pro is our pick
o1-pro is the better all-round choice, scoring 55/100 against Gemini 2.5 Computer Use Preview (46) and Aya Vision 32B (15). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGemini 2.5 Computer Use PreviewGemini 2.5 Computer Use Preview $3.44 · o1-pro $262.50 per 1M tokens (3:1 blend) · Aya Vision 32B unpriced
- Longest contexto1-proo1-pro 200,000 · Gemini 2.5 Computer Use Preview 128,000 · Aya Vision 32B 16,000 tokens
- Widest inputsSame inputsAya Vision 32B: Text, Images · Gemini 2.5 Computer Use Preview: Text, Images · o1-pro: Text, Images
- Self-hostingAya Vision 32BPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Aya Vision 32B | Gemini 2.5 Computer Use Preview | o1-pro |
|---|---|---|---|---|
| Inputs & features | 60% | 25 | 60 | 70 |
| Context window | 40% | 0 | 24 | 32 |
| Overall | 100% | 15/100 | 46/100 | 55/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Aya Vision 32BCohere | Gemini 2.5 Computer Use PreviewGoogle | o1-proOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $1.25 (best) | $150.00 |
| Output | — | $10.00 (best) | $600.00 |
| Cached input | — | — | — |
| Blended (3:1) | — | $3.44 (best) | $262.50 |
| Long-context rate | — | Over 200K: $2.50 / $15.00 | Same rate |
| Price source | — | Official Google API | Official OpenAI API |
| Limits | |||
| Context window | 16,000 tokens | 128,000 tokens | 200,000 tokens (best) |
| Max output | 4,000 tokens | 64,000 tokens | 100,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yeslow · medium · high |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Proprietary | Proprietary |
| API model ID | c4ai-aya-vision-32b | gemini-2.5-computer-use-preview-10-2025 | o1-pro |
| API providers | 1 | 2 | 5 (best) |
| Released | Mar 4, 2025 | Oct 7, 2025 | Mar 19, 2025 |
| Knowledge cutoff | — | Jan 2025 | Sep 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Aya Vision 32B—
- Gemini 2.5 Computer Use Preview$32.50
- o1-pro$2,700
Which should you choose?
Which is better: Aya Vision 32B, Gemini 2.5 Computer Use Preview or o1-pro?
o1-pro is the better all-round choice, scoring 55/100 against Gemini 2.5 Computer Use Preview (46) and Aya Vision 32B (15). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Aya Vision 32B, Gemini 2.5 Computer Use Preview or o1-pro?
Gemini 2.5 Computer Use Preview is cheaper at $1.25 input / $10.00 output per million tokens (official Google API price). o1-pro costs $150.00 input / $600.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $3.44 per million tokens for Gemini 2.5 Computer Use Preview versus $262.50 for o1-pro (76× as much). Aya Vision 32B has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Aya Vision 32B has not been scored yet, Gemini 2.5 Computer Use Preview has not been scored yet and o1-pro has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Aya Vision 32B, Gemini 2.5 Computer Use Preview and o1-pro yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Vision 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
o1-pro has the largest context window at 200,000 tokens, against 128,000 for Gemini 2.5 Computer Use Preview and 16,000 for Aya Vision 32B. Maximum output per response: Aya Vision 32B up to 4,000, Gemini 2.5 Computer Use Preview up to 64,000, o1-pro up to 100,000 tokens.
Which can read images, PDFs, audio or video?
Aya Vision 32B accepts text and images; Gemini 2.5 Computer Use Preview accepts text and images; o1-pro accepts text and images. They handle the same number of input types.
Are any of these open source?
Aya Vision 32B publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Gemini 2.5 Computer Use Preview and o1-pro is proprietary.
Which is newer?
Gemini 2.5 Computer Use Preview is the newest, released Oct 7, 2025. o1-pro came out Mar 19, 2025; Aya Vision 32B came out Mar 4, 2025. Knowledge cutoff: Gemini 2.5 Computer Use Preview Jan 2025, o1-pro Sep 2023.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
