GPT-5-Codex vs Gemini 2.5 Pro vs Gemini 2.5 Computer Use Preview
Gemini 2.5 Pro comes out ahead, 54 to 42 and 35 on our weighted score.
OpenAIGPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
- Our pick
GoogleGemini 2.5 Pro
54/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
- Google
Gemini 2.5 Computer Use Preview
35/100- ECI—
- Price$1.25 / $10.00
- Context128K
Gemini 2.5 Pro is our pick
Gemini 2.5 Pro is the better all-round choice, scoring 54/100 against GPT-5-Codex (42) and Gemini 2.5 Computer Use Preview (35). It leads on inputs & features and 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 priceSame priceGPT-5-Codex $3.44 · Gemini 2.5 Pro $3.44 · Gemini 2.5 Computer Use Preview $3.44 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · GPT-5-Codex 400,000 · Gemini 2.5 Computer Use Preview 128,000 tokens
- Widest inputsGemini 2.5 ProGPT-5-Codex: Text, Images · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video · Gemini 2.5 Computer Use Preview: Text, Images
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-5-Codex | Gemini 2.5 Pro | Gemini 2.5 Computer Use Preview |
|---|---|---|---|---|
| Price | 50% | 24 | 24 | 24 |
| Inputs & features | 30% | 70 | 100 | 60 |
| Context window | 20% | 44 | 61 | 24 |
| Overall | 100% | 42/100 | 54/100 | 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 | Gemini 2.5 ProGoogle | Gemini 2.5 Computer Use PreviewGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 145.3 | — |
| ECI rank | — | #78 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 85.3% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 24.6% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 84.7% | — |
| SWE-bench VerifiedFixing real GitHub issues | — | 57.6% | — |
| Price per million tokens | |||
| Input | $1.25 | $1.25 | $1.25 |
| Output | $10.00 | $10.00 | $10.00 |
| Cached input | — | $0.125 | — |
| Blended (3:1) | $3.44 | $3.44 | $3.44 |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | Over 200K: $2.50 / $15.00 |
| Price source | Median of 3 providers | Official Google API | Official Google API |
| Limits | |||
| Context window | 400,000 tokens | 1,048,576 tokens (best) | 128,000 tokens |
| Max output | 128,000 tokens (best) | 65,536 tokens | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | — | gemini-2.5-pro | gemini-2.5-computer-use-preview-10-2025 |
| API providers | 3 | 22 (best) | 2 |
| Released | Sep 15, 2025 | Jun 17, 2025 | Oct 7, 2025 |
| Knowledge cutoff | Sep 30, 2024 | Jan 2025 | 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
- Gemini 2.5 Pro$32.50
- Gemini 2.5 Computer Use Preview$32.50
Which should you choose?
Which is better: GPT-5-Codex, Gemini 2.5 Pro or Gemini 2.5 Computer Use Preview?
Gemini 2.5 Pro is the better all-round choice, scoring 54/100 against GPT-5-Codex (42) and Gemini 2.5 Computer Use Preview (35). It leads on inputs & features and 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, Gemini 2.5 Pro or Gemini 2.5 Computer Use Preview?
GPT-5-Codex, Gemini 2.5 Pro and Gemini 2.5 Computer Use Preview cost the same: $1.25 input / $10.00 output per million tokens.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5-Codex has not been scored yet, Gemini 2.5 Pro has an ECI of 145.3 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 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?
Gemini 2.5 Pro has the largest context window at 1,048,576 tokens, against 400,000 for GPT-5-Codex and 128,000 for Gemini 2.5 Computer Use Preview. Maximum output per response: GPT-5-Codex up to 128,000, Gemini 2.5 Pro up to 65,536, 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; Gemini 2.5 Pro accepts text, images, PDFs, audio and video; Gemini 2.5 Computer Use Preview accepts text and images. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
No. GPT-5-Codex, Gemini 2.5 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; Gemini 2.5 Pro came out Jun 17, 2025. Knowledge cutoff: GPT-5-Codex Sep 30, 2024, Gemini 2.5 Pro Jan 2025, 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.
