GPT-5.1 Chat vs MiniMax-M2.5 vs Qwen3.5 Plus
Qwen3.5 Plus comes out ahead, 59 to 48 and 38 on our weighted score, though MiniMax-M2.5 is 42% cheaper per token.
OpenAIGPT-5.1 Chat
38/100- ECI—
- Price$1.25 / $10.00
- Context128K
MiniMaxMiniMax-M2.5
48/100- ECI146.7
- Price$0.30 / $1.20
- Context205K
- Our pick
Alibaba (Qwen)Qwen3.5 Plus
59/100- ECI146.8
- Price$0.40 / $2.40
- Context1M
Qwen3.5 Plus is our pick
Qwen3.5 Plus is the better all-round choice, scoring 59/100 against MiniMax-M2.5 (48) and GPT-5.1 Chat (38). It leads on context window. MiniMax-M2.5 wins on price. 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-M2.5MiniMax-M2.5 $0.525 · Qwen3.5 Plus $0.90 · GPT-5.1 Chat $3.44 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 PlusQwen3.5 Plus 1,000,000 · MiniMax-M2.5 204,800 · GPT-5.1 Chat 128,000 tokens
- Widest inputsQwen3.5 PlusGPT-5.1 Chat: Text, Images · MiniMax-M2.5: Text · Qwen3.5 Plus: Text, Images, Video
- Self-hostingMiniMax-M2.5Publishes downloadable weights
| Measure | Weight | GPT-5.1 Chat | MiniMax-M2.5 | Qwen3.5 Plus |
|---|---|---|---|---|
| Price | 50% | 24 | 63 | 52 |
| Inputs & features | 30% | 70 | 35 | 70 |
| Context window | 20% | 24 | 32 | 60 |
| Overall | 100% | 38/100 | 48/100 | 59/100 |
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-5.1 ChatOpenAI | MiniMax-M2.5MiniMax | Qwen3.5 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 146.7 | 146.8 (best) |
| ECI rank | — | #66 of 148 | #65 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | — | 84.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 86.7% |
| 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 | — | $0.03 | — |
| Blended (3:1) | $3.44 | $0.525 (best) | $0.90 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official MiniMax (minimax.io) API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 204,800 tokens | 1,000,000 tokens (best) |
| Max output | 16,384 tokens | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | MiniMax-M2.5 | qwen3.5-plus |
| API providers | 2 | 21 (best) | 10 |
| Released | Nov 13, 2025 | Feb 12, 2026 | Feb 16, 2026 |
| Knowledge cutoff | Sep 30, 2024 | — | 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.
- GPT-5.1 Chat$32.50
- MiniMax-M2.5$5.40
- Qwen3.5 Plus$8.80
Which should you choose?
Which is better: GPT-5.1 Chat, MiniMax-M2.5 or Qwen3.5 Plus?
Qwen3.5 Plus is the better all-round choice, scoring 59/100 against MiniMax-M2.5 (48) and GPT-5.1 Chat (38). It leads on context window. MiniMax-M2.5 wins on price. 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-5.1 Chat, 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); GPT-5.1 Chat costs $1.25 input / $10.00 output per million tokens (median across 2 API providers). 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 GPT-5.1 Chat (6.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.1 Chat has not been scored yet, MiniMax-M2.5 has an ECI of 146.7 and Qwen3.5 Plus has an ECI of 146.8.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.1 Chat, MiniMax-M2.5 and Qwen3.5 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?
Qwen3.5 Plus has the largest context window at 1,000,000 tokens, against 204,800 for MiniMax-M2.5 and 128,000 for GPT-5.1 Chat. Maximum output per response: GPT-5.1 Chat up to 16,384, MiniMax-M2.5 up to 131,072, Qwen3.5 Plus up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GPT-5.1 Chat accepts text and images; MiniMax-M2.5 accepts text; Qwen3.5 Plus accepts text, images and video. Qwen3.5 Plus handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.5 publishes its weights and can be self-hosted; GPT-5.1 Chat 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; GPT-5.1 Chat came out Nov 13, 2025. Knowledge cutoff: GPT-5.1 Chat Sep 30, 2024, 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.
