GPT-5-Codex vs MiniMax-M2 vs Gemini 2.5 Computer Use Preview
MiniMax-M2 comes out ahead, 48 to 42 and 35 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
- Our pick
MiniMaxMiniMax-M2
48/100- ECI—
- Price$0.30 / $1.20
- Context205K
GoogleGemini 2.5 Computer Use Preview
35/100- ECI—
- Price$1.25 / $10.00
- Context128K
MiniMax-M2 is our pick
MiniMax-M2 is the better all-round choice, scoring 48/100 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 priceMiniMax-M2MiniMax-M2 $0.525 · GPT-5-Codex $3.44 · Gemini 2.5 Computer Use Preview $3.44 per 1M tokens (3:1 blend)
- Longest contextGPT-5-CodexGPT-5-Codex 400,000 · MiniMax-M2 204,800 · Gemini 2.5 Computer Use Preview 128,000 tokens
- Widest inputsGPT-5-Codex and Gemini 2.5 Computer Use PreviewGPT-5-Codex: Text, Images · MiniMax-M2: Text · Gemini 2.5 Computer Use Preview: Text, Images
- Self-hostingMiniMax-M2Publishes downloadable weights
| Measure | Weight | GPT-5-Codex | MiniMax-M2 | Gemini 2.5 Computer Use Preview |
|---|---|---|---|---|
| Price | 50% | 24 | 63 | 24 |
| Inputs & features | 30% | 70 | 35 | 60 |
| Context window | 20% | 44 | 32 | 24 |
| Overall | 100% | 42/100 | 48/100 | 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 | MiniMax-M2MiniMax | Gemini 2.5 Computer Use PreviewGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.25 | $0.30 (best) | $1.25 |
| Output | $10.00 | $1.20 (best) | $10.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 | $0.525 (best) | $3.44 |
| Long-context rate | Same rate | Same rate | Over 200K: $2.50 / $15.00 |
| Price source | Median of 3 providers | Official MiniMax (minimax.io) API | Official Google API |
| Limits | |||
| Context window | 400,000 tokens (best) | 204,800 tokens | 128,000 tokens |
| Max output | 128,000 tokens | 131,072 tokens (best) | 64,000 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 | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | MiniMax-M2 | gemini-2.5-computer-use-preview-10-2025 |
| API providers | 3 | 13 (best) | 2 |
| Released | Sep 15, 2025 | Oct 27, 2025 | Oct 7, 2025 |
| Knowledge cutoff | Sep 30, 2024 | — | 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
- MiniMax-M2$5.40
- Gemini 2.5 Computer Use Preview$32.50
Which should you choose?
Which is better: GPT-5-Codex, MiniMax-M2 or Gemini 2.5 Computer Use Preview?
MiniMax-M2 is the better all-round choice, scoring 48/100 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, GPT-5-Codex, MiniMax-M2 or Gemini 2.5 Computer Use Preview?
MiniMax-M2 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). GPT-5-Codex costs $1.25 input / $10.00 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). At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for MiniMax-M2 versus $3.44 for GPT-5-Codex (6.5× as much) and $3.44 for Gemini 2.5 Computer Use Preview (6.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5-Codex has not been scored yet, MiniMax-M2 has not been scored yet 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, MiniMax-M2 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?
GPT-5-Codex has the largest context window at 400,000 tokens, against 204,800 for MiniMax-M2 and 128,000 for Gemini 2.5 Computer Use Preview. Maximum output per response: GPT-5-Codex up to 128,000, MiniMax-M2 up to 131,072, 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; MiniMax-M2 accepts text; Gemini 2.5 Computer Use Preview accepts text and images. GPT-5-Codex handles the widest range of inputs.
Are any of these open source?
MiniMax-M2 publishes its weights and can be self-hosted; GPT-5-Codex and Gemini 2.5 Computer Use Preview is proprietary.
Which is newer?
MiniMax-M2 is the newest, released Oct 27, 2025. Gemini 2.5 Computer Use Preview came out Oct 7, 2025; GPT-5-Codex came out Sep 15, 2025. Knowledge cutoff: GPT-5-Codex Sep 30, 2024, 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.
