Gemini 2.5 Computer Use Preview vs Trinity Nano Preview vs GPT-5-Codex
GPT-5-Codex comes out ahead, 60 to 46 and 25 on our weighted score.
GoogleGemini 2.5 Computer Use Preview
46/100- ECI—
- Price$1.25 / $10.00
- Context128K
Arcee AITrinity Nano Preview
25/100- ECI—
- Price—
- Context131K
- Our pick
OpenAIGPT-5-Codex
60/100- ECI—
- Price$1.25 / $10.00
- Context400K
GPT-5-Codex is our pick
GPT-5-Codex is the better all-round choice, scoring 60/100 against Gemini 2.5 Computer Use Preview (46) and Trinity Nano Preview (25). 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 Preview and GPT-5-CodexGemini 2.5 Computer Use Preview $3.44 · GPT-5-Codex $3.44 per 1M tokens (3:1 blend) · Trinity Nano Preview unpriced
- Longest contextGPT-5-CodexGPT-5-Codex 400,000 · Trinity Nano Preview 131,072 · Gemini 2.5 Computer Use Preview 128,000 tokens
- Widest inputsGemini 2.5 Computer Use Preview and GPT-5-CodexGemini 2.5 Computer Use Preview: Text, Images · Trinity Nano Preview: Text · GPT-5-Codex: Text, Images
- Self-hostingTrinity Nano PreviewPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Gemini 2.5 Computer Use Preview | Trinity Nano Preview | GPT-5-Codex |
|---|---|---|---|---|
| Inputs & features | 60% | 60 | 25 | 70 |
| Context window | 40% | 24 | 24 | 44 |
| Overall | 100% | 46/100 | 25/100 | 60/100 |
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 | Gemini 2.5 Computer Use PreviewGoogle | Trinity Nano PreviewArcee AI | GPT-5-CodexOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.25 | — | $1.25 |
| Output | $10.00 | — | $10.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 | — | $3.44 |
| Long-context rate | Over 200K: $2.50 / $15.00 | — | Same rate |
| Price source | Official Google API | — | Median of 3 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens | 400,000 tokens (best) |
| Max output | 64,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | OpenOpenMDW-1.1 | Proprietary |
| API model ID | gemini-2.5-computer-use-preview-10-2025 | — | — |
| API providers | 2 | — | 3 (best) |
| Released | Oct 7, 2025 | Dec 1, 2025 | Sep 15, 2025 |
| Knowledge cutoff | Jan 2025 | — | 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
- Trinity Nano Preview—
- GPT-5-Codex$32.50
Which should you choose?
Which is better: Gemini 2.5 Computer Use Preview, Trinity Nano Preview or GPT-5-Codex?
GPT-5-Codex is the better all-round choice, scoring 60/100 against Gemini 2.5 Computer Use Preview (46) and Trinity Nano Preview (25). 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, Gemini 2.5 Computer Use Preview, Trinity Nano Preview 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). 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). Trinity Nano Preview has no published per-token price.
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, Trinity Nano Preview 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, Trinity Nano Preview 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 131,072 for Trinity Nano Preview and 128,000 for Gemini 2.5 Computer Use Preview. Maximum output per response: Gemini 2.5 Computer Use Preview up to 64,000, Trinity Nano Preview up to 131,072, 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; Trinity Nano Preview accepts text; GPT-5-Codex accepts text and images. Gemini 2.5 Computer Use Preview handles the widest range of inputs.
Are any of these open source?
Trinity Nano Preview publishes its weights (OpenMDW-1.1) and can be self-hosted; Gemini 2.5 Computer Use Preview and GPT-5-Codex is proprietary.
Which is newer?
Trinity Nano Preview is the newest, released Dec 1, 2025. Gemini 2.5 Computer Use Preview came out Oct 7, 2025; GPT-5-Codex came out Sep 15, 2025. Knowledge cutoff: Gemini 2.5 Computer Use Preview Jan 2025, 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.
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