Gemini 2.5 Computer Use Preview vs o1-pro vs GPT-5-Codex
GPT-5-Codex comes out ahead, 42 to 35 and 27 on our weighted score.
GoogleGemini 2.5 Computer Use Preview
35/100- ECI—
- Price$1.25 / $10.00
- Context128K
OpenAIo1-pro
27/100- ECI—
- Price$150.00 / $600.00
- Context200K
- Our pickOpenAI
GPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
GPT-5-Codex is our pick
GPT-5-Codex is the better all-round choice, scoring 42/100 against Gemini 2.5 Computer Use Preview (35) and o1-pro (27). It leads on context window. The score weighs price 50%, inputs & features 30%, context window 20%. 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 Preview and GPT-5-CodexGemini 2.5 Computer Use Preview $3.44 · GPT-5-Codex $3.44 · o1-pro $262.50 per 1M tokens (3:1 blend)
- Longest contextGPT-5-CodexGPT-5-Codex 400,000 · o1-pro 200,000 · Gemini 2.5 Computer Use Preview 128,000 tokens
- Widest inputsSame inputsGemini 2.5 Computer Use Preview: Text, Images · o1-pro: Text, Images · GPT-5-Codex: Text, Images
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Gemini 2.5 Computer Use Preview | o1-pro | GPT-5-Codex |
|---|---|---|---|---|
| Price | 50% | 24 | 0 | 24 |
| Inputs & features | 30% | 60 | 70 | 70 |
| Context window | 20% | 24 | 32 | 44 |
| Overall | 100% | 35/100 | 27/100 | 42/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 2.5 Computer Use PreviewGoogle | o1-proOpenAI | GPT-5-CodexOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.25 (best) | $150.00 | $1.25 (best) |
| Output | $10.00 (best) | $600.00 | $10.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 (best) | $262.50 | $3.44 (best) |
| Long-context rate | Over 200K: $2.50 / $15.00 | Same rate | Same rate |
| Price source | Official Google API | Official OpenAI API | Median of 3 providers |
| Limits | |||
| Context window | 128,000 tokens | 200,000 tokens | 400,000 tokens (best) |
| Max output | 64,000 tokens | 100,000 tokens | 128,000 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 | Yes | Yeslow · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gemini-2.5-computer-use-preview-10-2025 | o1-pro | — |
| API providers | 2 | 5 (best) | 3 |
| Released | Oct 7, 2025 | Mar 19, 2025 | Sep 15, 2025 |
| Knowledge cutoff | Jan 2025 | Sep 2023 | Sep 30, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 2.5 Computer Use Preview$32.50
- o1-pro$2,700
- GPT-5-Codex$32.50
Which should you choose?
Which is better: Gemini 2.5 Computer Use Preview, o1-pro or GPT-5-Codex?
GPT-5-Codex is the better all-round choice, scoring 42/100 against Gemini 2.5 Computer Use Preview (35) and o1-pro (27). It leads on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Gemini 2.5 Computer Use Preview, o1-pro or GPT-5-Codex?
Gemini 2.5 Computer Use Preview is cheaper at $1.25 input / $10.00 output per million tokens (official Google API price). GPT-5-Codex costs $1.25 input / $10.00 output per million tokens (median across 3 API providers); 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 $3.44 for GPT-5-Codex (1× as much) and $262.50 for o1-pro (76× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemini 2.5 Computer Use Preview has not been scored yet, o1-pro has not been scored yet and GPT-5-Codex has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.5 Computer Use Preview, o1-pro and GPT-5-Codex yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-5-Codex has the largest context window at 400,000 tokens, against 200,000 for o1-pro and 128,000 for Gemini 2.5 Computer Use Preview. Maximum output per response: Gemini 2.5 Computer Use Preview up to 64,000, o1-pro up to 100,000, GPT-5-Codex up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.5 Computer Use Preview accepts text and images; o1-pro accepts text and images; GPT-5-Codex accepts text and images. They handle the same number of input types.
Are any of these open source?
No. Gemini 2.5 Computer Use Preview, o1-pro and GPT-5-Codex are proprietary and only available through APIs and apps.
Which is newer?
Gemini 2.5 Computer Use Preview is the newest, released Oct 7, 2025. GPT-5-Codex came out Sep 15, 2025; o1-pro came out Mar 19, 2025. Knowledge cutoff: Gemini 2.5 Computer Use Preview Jan 2025, o1-pro Sep 2023, GPT-5-Codex Sep 30, 2024.
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.
