MiniMax-M2 vs Gemini 2.5 Computer Use Preview vs Qwen3-VL Plus
Qwen3-VL Plus comes out ahead, 56 to 48 and 35 on our weighted score.
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
- Our pick
Alibaba (Qwen)Qwen3-VL Plus
56/100- ECI—
- Price$0.20 / $1.60
- Context262K
Qwen3-VL Plus is our pick
Qwen3-VL Plus is the better all-round choice, scoring 56/100 against MiniMax-M2 (48) and Gemini 2.5 Computer Use Preview (35). It leads on 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 · Qwen3-VL Plus $0.55 · Gemini 2.5 Computer Use Preview $3.44 per 1M tokens (3:1 blend)
- Longest contextQwen3-VL PlusQwen3-VL Plus 262,144 · MiniMax-M2 204,800 · Gemini 2.5 Computer Use Preview 128,000 tokens
- Widest inputsGemini 2.5 Computer Use Preview and Qwen3-VL PlusMiniMax-M2: Text · Gemini 2.5 Computer Use Preview: Text, Images · Qwen3-VL Plus: Text, Images
- Self-hostingMiniMax-M2Publishes downloadable weights
| Measure | Weight | MiniMax-M2 | Gemini 2.5 Computer Use Preview | Qwen3-VL Plus |
|---|---|---|---|---|
| Price | 50% | 63 | 24 | 62 |
| Inputs & features | 30% | 35 | 60 | 60 |
| Context window | 20% | 32 | 24 | 37 |
| Overall | 100% | 48/100 | 35/100 | 56/100 |
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 | MiniMax-M2MiniMax | Gemini 2.5 Computer Use PreviewGoogle | Qwen3-VL PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $1.25 | $0.20 (best) |
| Output | $1.20 (best) | $10.00 | $1.60 |
| Cached input | — | — | — |
| Blended (3:1) | $0.525 (best) | $3.44 | $0.55 |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | Same rate |
| Price source | Official MiniMax (minimax.io) API | Official Google API | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 64,000 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | 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 | No |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | MiniMax-M2 | gemini-2.5-computer-use-preview-10-2025 | qwen3-vl-plus |
| API providers | 13 (best) | 2 | 6 |
| Released | Oct 27, 2025 | Oct 7, 2025 | Sep 23, 2025 |
| Knowledge cutoff | — | Jan 2025 | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiniMax-M2$5.40
- Gemini 2.5 Computer Use Preview$32.50
- Qwen3-VL Plus$5.20
Which should you choose?
Which is better: MiniMax-M2, Gemini 2.5 Computer Use Preview or Qwen3-VL Plus?
Qwen3-VL Plus is the better all-round choice, scoring 56/100 against MiniMax-M2 (48) and Gemini 2.5 Computer Use Preview (35). It leads on 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, MiniMax-M2, Gemini 2.5 Computer Use Preview or Qwen3-VL Plus?
MiniMax-M2 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). Qwen3-VL Plus costs $0.20 input / $1.60 output per million tokens (official Alibaba API price); 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 $0.55 for Qwen3-VL Plus (1× 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. MiniMax-M2 has not been scored yet, Gemini 2.5 Computer Use Preview has not been scored yet and Qwen3-VL Plus has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2, Gemini 2.5 Computer Use Preview and Qwen3-VL Plus 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?
Qwen3-VL Plus has the largest context window at 262,144 tokens, against 204,800 for MiniMax-M2 and 128,000 for Gemini 2.5 Computer Use Preview. Maximum output per response: MiniMax-M2 up to 131,072, Gemini 2.5 Computer Use Preview up to 64,000, Qwen3-VL Plus up to 32,768 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2 accepts text; Gemini 2.5 Computer Use Preview accepts text and images; Qwen3-VL Plus accepts text and images. Gemini 2.5 Computer Use Preview handles the widest range of inputs.
Are any of these open source?
MiniMax-M2 publishes its weights and can be self-hosted; Gemini 2.5 Computer Use Preview and Qwen3-VL Plus 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; Qwen3-VL Plus came out Sep 23, 2025. Knowledge cutoff: Gemini 2.5 Computer Use Preview Jan 2025, Qwen3-VL Plus Apr 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.
