Gemini 2.5 Computer Use Preview vs Kimi K2 Thinking Turbo vs GPT-5-Codex
GPT-5-Codex comes out ahead, 60 to 46 and 36 on our weighted score.
GoogleGemini 2.5 Computer Use Preview
46/100- ECI—
- Price$1.25 / $10.00
- Context128K
Moonshot AIKimi K2 Thinking Turbo
36/100- ECI—
- Price—
- Context262K
- 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 Kimi K2 Thinking Turbo (36). 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) · Kimi K2 Thinking Turbo unpriced
- Longest contextGPT-5-CodexGPT-5-Codex 400,000 · Kimi K2 Thinking Turbo 262,144 · 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 · Kimi K2 Thinking Turbo: Text · GPT-5-Codex: Text, Images
- Self-hostingKimi K2 Thinking TurboPublishes downloadable weights
| Measure | Weight | Gemini 2.5 Computer Use Preview | Kimi K2 Thinking Turbo | GPT-5-Codex |
|---|---|---|---|---|
| Inputs & features | 60% | 60 | 35 | 70 |
| Context window | 40% | 24 | 37 | 44 |
| Overall | 100% | 46/100 | 36/100 | 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 | Kimi K2 Thinking TurboMoonshot 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 | 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 | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | gemini-2.5-computer-use-preview-10-2025 | — | — |
| API providers | 2 | — | 3 (best) |
| Released | Oct 7, 2025 | Nov 6, 2025 | Sep 15, 2025 |
| Knowledge cutoff | Jan 2025 | Aug 2024 | 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
- Kimi K2 Thinking Turbo—
- GPT-5-Codex$32.50
Which should you choose?
Which is better: Gemini 2.5 Computer Use Preview, Kimi K2 Thinking Turbo 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 Kimi K2 Thinking Turbo (36). 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, Kimi K2 Thinking Turbo 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). Kimi K2 Thinking Turbo 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, Kimi K2 Thinking Turbo 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, Kimi K2 Thinking Turbo 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 Kimi K2 Thinking Turbo and 128,000 for Gemini 2.5 Computer Use Preview. Maximum output per response: Gemini 2.5 Computer Use Preview up to 64,000, Kimi K2 Thinking Turbo 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; Kimi K2 Thinking Turbo 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?
Kimi K2 Thinking Turbo publishes its weights and can be self-hosted; Gemini 2.5 Computer Use Preview and GPT-5-Codex is proprietary.
Which is newer?
Kimi K2 Thinking Turbo is the newest, released Nov 6, 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, Kimi K2 Thinking Turbo Aug 2024, 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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