Qwen3.5 Plus vs Gemini 2.5 Pro vs MiniMax-M2.5
Qwen3.5 Plus comes out ahead, 66 to 63 and 61 on our weighted score, though MiniMax-M2.5 is 42% cheaper per token.
- Our pick
Alibaba (Qwen)
Qwen3.5 Plus
66/100- ECI146.8
- Price$0.40 / $2.40
- Context1M
Google
Gemini 2.5 Pro
63/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
MiniMax
MiniMax-M2.5
61/100- ECI146.7
- Price$0.30 / $1.20
- Context205K
Qwen3.5 Plus is our pick
Qwen3.5 Plus is the better all-round choice, scoring 66/100 against Gemini 2.5 Pro (63) and MiniMax-M2.5 (61). Gemini 2.5 Pro wins on inputs & features. MiniMax-M2.5 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.5 PlusCapabilities Index (ECI): Qwen3.5 Plus 146.8 · MiniMax-M2.5 146.7 · Gemini 2.5 Pro 145.3
- Lowest priceMiniMax-M2.5MiniMax-M2.5 $0.525 · Qwen3.5 Plus $0.90 · Gemini 2.5 Pro $3.44 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · Qwen3.5 Plus 1,000,000 · MiniMax-M2.5 204,800 tokens
- Widest inputsGemini 2.5 ProQwen3.5 Plus: Text, Images, Video · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video · MiniMax-M2.5: Text
- Self-hostingMiniMax-M2.5Publishes downloadable weights
| Measure | Weight | Qwen3.5 Plus | Gemini 2.5 Pro | MiniMax-M2.5 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 74 | 72 | 74 |
| Price | 25% | 52 | 24 | 63 |
| Inputs & features | 15% | 70 | 100 | 35 |
| Context window | 10% | 60 | 61 | 32 |
| Overall | 100% | 66/100 | 63/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.8 (best) | 145.3 | 146.7 |
| ECI rank | #65 of 148 (best) | #78 of 148 | #66 of 148 |
| GPQA DiamondGraduate-level science questions | 84.9% | 85.3% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 24.6% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 86.7% (best) | 84.7% | — |
| SWE-bench VerifiedFixing real GitHub issues | — | 57.6% | — |
| SimpleQA VerifiedShort factual questions | 25.4% | — | — |
| Price per million tokens | |||
| Input | $0.40 | $1.25 | $0.30 (best) |
| Output | $2.40 | $10.00 | $1.20 (best) |
| Cached input | — | $0.125 | $0.03 (best) |
| Blended (3:1) | $0.90 | $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 | 1,000,000 tokens | 1,048,576 tokens (best) | 204,800 tokens |
| Max output | 65,536 tokens | 65,536 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | Yes | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | qwen3.5-plus | gemini-2.5-pro | MiniMax-M2.5 |
| API providers | 10 | 22 (best) | 21 |
| Released | Feb 16, 2026 | Jun 17, 2025 | Feb 12, 2026 |
| 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.
Qwen3.5 Plus$8.80
Gemini 2.5 Pro$32.50
MiniMax-M2.5$5.40
Which should you choose?
Which is better: Qwen3.5 Plus, Gemini 2.5 Pro or MiniMax-M2.5?
Qwen3.5 Plus is the better all-round choice, scoring 66/100 against Gemini 2.5 Pro (63) and MiniMax-M2.5 (61). Gemini 2.5 Pro wins on inputs & features. MiniMax-M2.5 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.5 Plus, Gemini 2.5 Pro or MiniMax-M2.5?
MiniMax-M2.5 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). Qwen3.5 Plus costs $0.40 input / $2.40 output per million tokens (official Alibaba API price); Gemini 2.5 Pro 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-M2.5 versus $0.90 for Qwen3.5 Plus (1.7× as much) and $3.44 for Gemini 2.5 Pro (6.5× as much).
Which scores higher on benchmarks?
Qwen3.5 Plus scores higher on the Capabilities Index (ECI): Qwen3.5 Plus 146.8 (#65 of 148), MiniMax-M2.5 146.7 (#66 of 148) and Gemini 2.5 Pro 145.3 (#78 of 148). The confidence ranges of the top two overlap (144.6–148.1 vs 142.3–147.9), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 Plus and MiniMax-M2.5 yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 Plus leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Gemini 2.5 Pro has the largest context window at 1,048,576 tokens, against 1,000,000 for Qwen3.5 Plus and 204,800 for MiniMax-M2.5. Maximum output per response: Qwen3.5 Plus up to 65,536, Gemini 2.5 Pro up to 65,536, MiniMax-M2.5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3.5 Plus accepts text, images and video; Gemini 2.5 Pro accepts text, images, PDFs, audio and video; MiniMax-M2.5 accepts text. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.5 publishes its weights and can be self-hosted; Qwen3.5 Plus and Gemini 2.5 Pro is proprietary.
Which is newer?
Qwen3.5 Plus is the newest, released Feb 16, 2026. MiniMax-M2.5 came out Feb 12, 2026; Gemini 2.5 Pro came out Jun 17, 2025. Knowledge cutoff: Qwen3.5 Plus Apr 2025, Gemini 2.5 Pro 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.