o1-pro vs Gemini 2.5 Computer Use Preview vs Aya Vision 32B
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.
- Our pick
OpenAIo1-pro
55/100- ECI—
- Price$150.00 / $600.00
- Context200K
GoogleGemini 2.5 Computer Use Preview
46/100- ECI—
- Price$1.25 / $10.00
- Context128K
CohereAya Vision 32B
15/100- ECI—
- Price—
- Context16K
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 inputso1-pro: Text, Images · Gemini 2.5 Computer Use Preview: Text, Images · Aya Vision 32B: Text, Images
- Self-hostingAya Vision 32BPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | o1-pro | Gemini 2.5 Computer Use Preview | Aya Vision 32B |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 60 | 25 |
| Context window | 40% | 32 | 24 | 0 |
| Overall | 100% | 55/100 | 46/100 | 15/100 |
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 | o1-proOpenAI | Gemini 2.5 Computer Use PreviewGoogle | Aya Vision 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $150.00 | $1.25 (best) | — |
| Output | $600.00 | $10.00 (best) | — |
| Cached input | — | — | — |
| Blended (3:1) | $262.50 | $3.44 (best) | — |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | — |
| Price source | Official OpenAI API | Official Google API | — |
| Limits | |||
| Context window | 200,000 tokens (best) | 128,000 tokens | 16,000 tokens |
| Max output | 100,000 tokens (best) | 64,000 tokens | 4,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high | Yes | No |
| Tool calling | Yes | Yes | No |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | OpenCC-BY-NC-4.0 |
| API model ID | o1-pro | gemini-2.5-computer-use-preview-10-2025 | c4ai-aya-vision-32b |
| API providers | 5 (best) | 2 | 1 |
| Released | Mar 19, 2025 | Oct 7, 2025 | Mar 4, 2025 |
| Knowledge cutoff | Sep 2023 | Jan 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o1-pro$2,700
- Gemini 2.5 Computer Use Preview$32.50
- Aya Vision 32B—
Which should you choose?
Which is better: o1-pro, Gemini 2.5 Computer Use Preview or Aya Vision 32B?
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, o1-pro, Gemini 2.5 Computer Use Preview or Aya Vision 32B?
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. o1-pro has not been scored yet, Gemini 2.5 Computer Use Preview has not been scored yet and Aya Vision 32B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for o1-pro, Gemini 2.5 Computer Use Preview and Aya Vision 32B 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: o1-pro up to 100,000, Gemini 2.5 Computer Use Preview up to 64,000, Aya Vision 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
o1-pro accepts text and images; Gemini 2.5 Computer Use Preview accepts text and images; Aya Vision 32B 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; o1-pro and Gemini 2.5 Computer Use Preview 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: o1-pro Sep 2023, Gemini 2.5 Computer Use Preview Jan 2025.
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.
