GPT-5-Codex vs o1-pro vs Gemini 2.5 Computer Use Preview
GPT-5-Codex comes out ahead, 42 to 35 and 27 on our weighted score.
- Our pick
OpenAIGPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
- OpenAI
o1-pro
27/100- ECI—
- Price$150.00 / $600.00
- Context200K
GoogleGemini 2.5 Computer Use Preview
35/100- ECI—
- Price$1.25 / $10.00
- Context128K
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 priceGPT-5-Codex and Gemini 2.5 Computer Use PreviewGPT-5-Codex $3.44 · Gemini 2.5 Computer Use Preview $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 inputsGPT-5-Codex: Text, Images · o1-pro: Text, Images · Gemini 2.5 Computer Use Preview: Text, Images
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-5-Codex | o1-pro | Gemini 2.5 Computer Use Preview |
|---|---|---|---|---|
| Price | 50% | 24 | 0 | 24 |
| Inputs & features | 30% | 70 | 70 | 60 |
| Context window | 20% | 44 | 32 | 24 |
| Overall | 100% | 42/100 | 27/100 | 35/100 |
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 | GPT-5-CodexOpenAI | o1-proOpenAI | Gemini 2.5 Computer Use PreviewGoogle |
|---|---|---|---|
| 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 | Same rate | Same rate | Over 200K: $2.50 / $15.00 |
| Price source | Median of 3 providers | Official OpenAI API | Official Google API |
| Limits | |||
| Context window | 400,000 tokens (best) | 200,000 tokens | 128,000 tokens |
| Max output | 128,000 tokens (best) | 100,000 tokens | 64,000 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 | Yes | Yeslow · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | — | o1-pro | gemini-2.5-computer-use-preview-10-2025 |
| API providers | 3 | 5 (best) | 2 |
| Released | Sep 15, 2025 | Mar 19, 2025 | Oct 7, 2025 |
| Knowledge cutoff | Sep 30, 2024 | 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.
- GPT-5-Codex$32.50
- o1-pro$2,700
- Gemini 2.5 Computer Use Preview$32.50
Which should you choose?
Which is better: GPT-5-Codex, o1-pro or Gemini 2.5 Computer Use Preview?
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, GPT-5-Codex, o1-pro or Gemini 2.5 Computer Use Preview?
GPT-5-Codex is cheaper at $1.25 input / $10.00 output per million tokens (median across 3 API providers). Gemini 2.5 Computer Use Preview costs $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 GPT-5-Codex versus $3.44 for Gemini 2.5 Computer Use Preview (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. GPT-5-Codex has not been scored yet, o1-pro has not been scored yet and Gemini 2.5 Computer Use Preview has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5-Codex, o1-pro and Gemini 2.5 Computer Use Preview 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: GPT-5-Codex up to 128,000, o1-pro up to 100,000, Gemini 2.5 Computer Use Preview up to 64,000 tokens.
Which can read images, PDFs, audio or video?
GPT-5-Codex accepts text and images; o1-pro accepts text and images; Gemini 2.5 Computer Use Preview accepts text and images. They handle the same number of input types.
Are any of these open source?
No. GPT-5-Codex, o1-pro and Gemini 2.5 Computer Use Preview 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: GPT-5-Codex Sep 30, 2024, 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.
