Apertus 70B vs Gemini 2.5 Computer Use Preview vs GPT-5-Codex
GPT-5-Codex comes out ahead, 42 to 35 and 33 on our weighted score, though Apertus 70B is 2.8× cheaper per token.
Swiss AI
Apertus 70B
33/100- ECI—
- Price$0.82 / $2.42
- Context66K
GoogleGemini 2.5 Computer Use Preview
35/100- ECI—
- Price$1.25 / $10.00
- Context128K
- Our pick
OpenAIGPT-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 Apertus 70B (33). It leads on inputs & features and context window. Apertus 70B wins on price. 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 priceApertus 70BApertus 70B $1.22 · 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 · Gemini 2.5 Computer Use Preview 128,000 · Apertus 70B 65,536 tokens
- Widest inputsGemini 2.5 Computer Use Preview and GPT-5-CodexApertus 70B: Text · Gemini 2.5 Computer Use Preview: Text, Images · GPT-5-Codex: Text, Images
- Self-hostingApertus 70BPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Apertus 70B | Gemini 2.5 Computer Use Preview | GPT-5-Codex |
|---|---|---|---|---|
| Price | 50% | 46 | 24 | 24 |
| Inputs & features | 30% | 25 | 60 | 70 |
| Context window | 20% | 12 | 24 | 44 |
| Overall | 100% | 33/100 | 35/100 | 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 | Apertus 70BSwiss AI | Gemini 2.5 Computer Use PreviewGoogle | GPT-5-CodexOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.82 (best) | $1.25 | $1.25 |
| Output | $2.42 (best) | $10.00 | $10.00 |
| Cached input | — | — | — |
| Blended (3:1) | $1.22 (best) | $3.44 | $3.44 |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | Same rate |
| Price source | Median of 3 providers | Official Google API | Median of 3 providers |
| Limits | |||
| Context window | 65,536 tokens | 128,000 tokens | 400,000 tokens (best) |
| Max output | 8,192 tokens | 64,000 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenApache-2.0 | Proprietary | Proprietary |
| API model ID | — | gemini-2.5-computer-use-preview-10-2025 | — |
| API providers | 3 (best) | 2 | 3 (best) |
| Released | Sep 2, 2025 | Oct 7, 2025 | Sep 15, 2025 |
| Knowledge cutoff | Sep 2025 | 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.
- Apertus 70B$13.04
- Gemini 2.5 Computer Use Preview$32.50
- GPT-5-Codex$32.50
Which should you choose?
Which is better: Apertus 70B, Gemini 2.5 Computer Use Preview 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 Apertus 70B (33). It leads on inputs & features and context window. Apertus 70B wins on price. 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, Apertus 70B, Gemini 2.5 Computer Use Preview or GPT-5-Codex?
Apertus 70B is cheaper at $0.82 input / $2.42 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); 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 $1.22 per million tokens for Apertus 70B versus $3.44 for Gemini 2.5 Computer Use Preview (2.8× as much) and $3.44 for GPT-5-Codex (2.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Apertus 70B has not been scored yet, Gemini 2.5 Computer Use 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 Apertus 70B, Gemini 2.5 Computer Use 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 128,000 for Gemini 2.5 Computer Use Preview and 65,536 for Apertus 70B. Maximum output per response: Apertus 70B up to 8,192, Gemini 2.5 Computer Use Preview up to 64,000, GPT-5-Codex up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Apertus 70B accepts text; Gemini 2.5 Computer Use Preview accepts text and images; 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?
Apertus 70B 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?
Gemini 2.5 Computer Use Preview is the newest, released Oct 7, 2025. GPT-5-Codex came out Sep 15, 2025; Apertus 70B came out Sep 2, 2025. Knowledge cutoff: Apertus 70B Sep 2025, 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.
