Gemini 2.5 Pro vs MiniMax-M2.5 vs Qwen3.5 Plus
Qwen3.5 Plus comes out ahead, 66 to 63 and 61 on our weighted score, though MiniMax-M2.5 is 42% cheaper per token.
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
- Our pick
Alibaba (Qwen)
Qwen3.5 Plus
66/100- ECI146.8
- Price$0.40 / $2.40
- Context1M
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 ProGemini 2.5 Pro: Text, Images, PDFs, Audio, Video · MiniMax-M2.5: Text · Qwen3.5 Plus: Text, Images, Video
- Self-hostingMiniMax-M2.5Publishes downloadable weights
| Measure | Weight | Gemini 2.5 Pro | MiniMax-M2.5 | Qwen3.5 Plus |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 72 | 74 | 74 |
| Price | 25% | 24 | 63 | 52 |
| Inputs & features | 15% | 100 | 35 | 70 |
| Context window | 10% | 61 | 32 | 60 |
| Overall | 100% | 63/100 | 61/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.3 | 146.7 | 146.8 (best) |
| ECI rank | #78 of 148 | #66 of 148 | #65 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 85.3% (best) | — | 84.9% |
| FrontierMath Tiers 1–3Research-level mathematics | 24.6% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 84.7% | — | 86.7% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 57.6% | — | — |
| SimpleQA VerifiedShort factual questions | — | — | 25.4% |
| Price per million tokens | |||
| Input | $1.25 | $0.30 (best) | $0.40 |
| Output | $10.00 | $1.20 (best) | $2.40 |
| Cached input | $0.125 | $0.03 (best) | — |
| Blended (3:1) | $3.44 | $0.525 (best) | $0.90 |
| Long-context rate | Over 200K: $2.50 / $15.00 | Same rate | Same rate |
| Price source | Official Google API | Official MiniMax (minimax.io) API | Official Alibaba API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 204,800 tokens | 1,000,000 tokens |
| Max output | 65,536 tokens | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | gemini-2.5-pro | MiniMax-M2.5 | qwen3.5-plus |
| API providers | 22 (best) | 21 | 10 |
| Released | Jun 17, 2025 | Feb 12, 2026 | Feb 16, 2026 |
| 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.
Gemini 2.5 Pro$32.50
MiniMax-M2.5$5.40
Qwen3.5 Plus$8.80
Which should you choose?
Which is better: Gemini 2.5 Pro, MiniMax-M2.5 or Qwen3.5 Plus?
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, Gemini 2.5 Pro, MiniMax-M2.5 or Qwen3.5 Plus?
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 MiniMax-M2.5 and Qwen3.5 Plus 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: Gemini 2.5 Pro up to 65,536, MiniMax-M2.5 up to 131,072, Qwen3.5 Plus up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.5 Pro accepts text, images, PDFs, audio and video; MiniMax-M2.5 accepts text; Qwen3.5 Plus accepts text, images and video. 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; Gemini 2.5 Pro and Qwen3.5 Plus 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: Gemini 2.5 Pro Jan 2025, Qwen3.5 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.