Qwen3-VL Plus vs Gemini 2.5 Computer Use Preview vs MiniMax-M2
Qwen3-VL Plus comes out ahead, 56 to 48 and 35 on our weighted score.
- Our pick
Alibaba (Qwen)Qwen3-VL Plus
56/100- ECI—
- Price$0.20 / $1.60
- Context262K
GoogleGemini 2.5 Computer Use Preview
35/100- ECI—
- Price$1.25 / $10.00
- Context128K
MiniMaxMiniMax-M2
48/100- ECI—
- Price$0.30 / $1.20
- Context205K
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 inputsQwen3-VL Plus and Gemini 2.5 Computer Use PreviewQwen3-VL Plus: Text, Images · Gemini 2.5 Computer Use Preview: Text, Images · MiniMax-M2: Text
- Self-hostingMiniMax-M2Publishes downloadable weights
| Measure | Weight | Qwen3-VL Plus | Gemini 2.5 Computer Use Preview | MiniMax-M2 |
|---|---|---|---|---|
| Price | 50% | 62 | 24 | 63 |
| Inputs & features | 30% | 60 | 60 | 35 |
| Context window | 20% | 37 | 24 | 32 |
| Overall | 100% | 56/100 | 35/100 | 48/100 |
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 | Qwen3-VL PlusAlibaba (Qwen) | Gemini 2.5 Computer Use PreviewGoogle | MiniMax-M2MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.20 (best) | $1.25 | $0.30 |
| Output | $1.60 | $10.00 | $1.20 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.55 | $3.44 | $0.525 (best) |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | Same rate |
| Price source | Official Alibaba API | Official Google API | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 262,144 tokens (best) | 128,000 tokens | 204,800 tokens |
| Max output | 32,768 tokens | 64,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| 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 | Proprietary | Proprietary | Open |
| API model ID | qwen3-vl-plus | gemini-2.5-computer-use-preview-10-2025 | MiniMax-M2 |
| API providers | 6 | 2 | 13 (best) |
| Released | Sep 23, 2025 | Oct 7, 2025 | Oct 27, 2025 |
| Knowledge cutoff | Apr 2025 | 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.
- Qwen3-VL Plus$5.20
- Gemini 2.5 Computer Use Preview$32.50
- MiniMax-M2$5.40
Which should you choose?
Which is better: Qwen3-VL Plus, Gemini 2.5 Computer Use Preview or MiniMax-M2?
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, Qwen3-VL Plus, Gemini 2.5 Computer Use Preview or MiniMax-M2?
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. Qwen3-VL Plus has not been scored yet, Gemini 2.5 Computer Use Preview has not been scored yet and MiniMax-M2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3-VL Plus, Gemini 2.5 Computer Use Preview and MiniMax-M2 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: Qwen3-VL Plus up to 32,768, Gemini 2.5 Computer Use Preview up to 64,000, MiniMax-M2 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3-VL Plus accepts text and images; Gemini 2.5 Computer Use Preview accepts text and images; MiniMax-M2 accepts text. Qwen3-VL Plus handles the widest range of inputs.
Are any of these open source?
MiniMax-M2 publishes its weights and can be self-hosted; Qwen3-VL Plus 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; Qwen3-VL Plus came out Sep 23, 2025. Knowledge cutoff: Qwen3-VL Plus Apr 2025, 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.
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