Gemini 2.5 Computer Use Preview vs Ministral 14B vs GPT-5-Codex
Ministral 14B comes out ahead, 64 to 42 and 35 on our weighted score, and it is the cheaper option too.
GoogleGemini 2.5 Computer Use Preview
35/100- ECI—
- Price$1.25 / $10.00
- Context128K
- Our pick
Mistral AIMinistral 14B
64/100- ECI—
- Price$0.20 / $0.20
- Context262K
OpenAIGPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
Ministral 14B is our pick
Ministral 14B is the better all-round choice, scoring 64/100 against GPT-5-Codex (42) and Gemini 2.5 Computer Use Preview (35). It leads on price. GPT-5-Codex wins 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 priceMinistral 14BMinistral 14B $0.20 · Gemini 2.5 Computer Use Preview $3.44 · GPT-5-Codex $3.44 per 1M tokens (3:1 blend)
- Longest contextGPT-5-CodexGPT-5-Codex 400,000 · Ministral 14B 262,144 · Gemini 2.5 Computer Use Preview 128,000 tokens
- Widest inputsSame inputsGemini 2.5 Computer Use Preview: Text, Images · Ministral 14B: Text, Images · GPT-5-Codex: Text, Images
- Self-hostingMinistral 14BPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Gemini 2.5 Computer Use Preview | Ministral 14B | GPT-5-Codex |
|---|---|---|---|---|
| Price | 50% | 24 | 83 | 24 |
| Inputs & features | 30% | 60 | 50 | 70 |
| Context window | 20% | 24 | 37 | 44 |
| Overall | 100% | 35/100 | 64/100 | 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 | Ministral 14BMistral AI | GPT-5-CodexOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.25 | $0.20 (best) | $1.25 |
| Output | $10.00 | $0.20 (best) | $10.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 | $0.20 (best) | $3.44 |
| Long-context rate | Over 200K: $2.50 / $15.00 | Same rate | Same rate |
| Price source | Official Google API | Median of 1 providers | Median of 3 providers |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens | 400,000 tokens (best) |
| Max output | 64,000 tokens | 262,144 tokens (best) | 128,000 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 | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | OpenApache-2.0 | Proprietary |
| API model ID | gemini-2.5-computer-use-preview-10-2025 | — | — |
| API providers | 2 | 1 | 3 (best) |
| Released | Oct 7, 2025 | Dec 2, 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
- Ministral 14B$2.40
- GPT-5-Codex$32.50
Which should you choose?
Which is better: Gemini 2.5 Computer Use Preview, Ministral 14B or GPT-5-Codex?
Ministral 14B is the better all-round choice, scoring 64/100 against GPT-5-Codex (42) and Gemini 2.5 Computer Use Preview (35). It leads on price. GPT-5-Codex wins 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, Gemini 2.5 Computer Use Preview, Ministral 14B or GPT-5-Codex?
Ministral 14B is cheaper at $0.20 input / $0.20 output per million tokens (median across 1 API provider). Gemini 2.5 Computer Use Preview costs $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 $0.20 per million tokens for Ministral 14B versus $3.44 for Gemini 2.5 Computer Use Preview (17× as much) and $3.44 for GPT-5-Codex (17× 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, Ministral 14B 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, Ministral 14B 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 262,144 for Ministral 14B and 128,000 for Gemini 2.5 Computer Use Preview. Maximum output per response: Gemini 2.5 Computer Use Preview up to 64,000, Ministral 14B up to 262,144, 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; Ministral 14B 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?
Ministral 14B publishes its weights (Apache-2.0) and can be self-hosted; Gemini 2.5 Computer Use Preview and GPT-5-Codex is proprietary.
Which is newer?
Ministral 14B is the newest, released Dec 2, 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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