GLM-4.6V vs MiniMax-M2.1 vs MiniMax-M2.5
GLM-4.6V comes out ahead, 59 to 48 and 48 on our weighted score, and it is the cheaper option too.
- Our pick
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
MiniMaxMiniMax-M2.1
48/100- ECI—
- Price$0.30 / $1.20
- Context205K
- MiniMax
MiniMax-M2.5
48/100- ECI146.7
- Price$0.30 / $1.20
- Context205K
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 59/100 against MiniMax-M2.1 (48) and MiniMax-M2.5 (48). It leads on price and inputs & features. 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 priceGLM-4.6VGLM-4.6V $0.45 · MiniMax-M2.1 $0.525 · MiniMax-M2.5 $0.525 per 1M tokens (3:1 blend)
- Longest contextMiniMax-M2.1 and MiniMax-M2.5MiniMax-M2.1 204,800 · MiniMax-M2.5 204,800 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VGLM-4.6V: Text, Images, Video · MiniMax-M2.1: Text · MiniMax-M2.5: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V | MiniMax-M2.1 | MiniMax-M2.5 |
|---|---|---|---|---|
| Price | 50% | 66 | 63 | 63 |
| Inputs & features | 30% | 70 | 35 | 35 |
| Context window | 20% | 24 | 32 | 32 |
| Overall | 100% | 59/100 | 48/100 | 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 | GLM-4.6VZ.ai (Zhipu) | MiniMax-M2.1MiniMax | MiniMax-M2.5MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 146.7 |
| ECI rank | — | — | #66 of 148 |
| Price per million tokens | |||
| Input | $0.30 | $0.30 | $0.30 |
| Output | $0.90 (best) | $1.20 | $1.20 |
| Cached input | — | $0.03 | $0.03 |
| Blended (3:1) | $0.45 (best) | $0.525 | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official MiniMax (minimax.io) API | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 128,000 tokens | 204,800 tokens (best) | 204,800 tokens (best) |
| Max output | 32,768 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.6v | MiniMax-M2.1 | MiniMax-M2.5 |
| API providers | 10 | 13 | 21 (best) |
| Released | Dec 8, 2025 | Dec 23, 2025 | Feb 12, 2026 |
| Knowledge cutoff | 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.
- GLM-4.6V$4.80
- MiniMax-M2.1$5.40
- MiniMax-M2.5$5.40
Which should you choose?
Which is better: GLM-4.6V, MiniMax-M2.1 or MiniMax-M2.5?
GLM-4.6V is the better all-round choice, scoring 59/100 against MiniMax-M2.1 (48) and MiniMax-M2.5 (48). It leads on price and inputs & features. 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, GLM-4.6V, MiniMax-M2.1 or MiniMax-M2.5?
GLM-4.6V is cheaper at $0.30 input / $0.90 output per million tokens (official Z.AI API price). MiniMax-M2.1 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price); MiniMax-M2.5 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for GLM-4.6V versus $0.525 for MiniMax-M2.1 (1.2× as much) and $0.525 for MiniMax-M2.5 (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V has not been scored yet, MiniMax-M2.1 has not been scored yet and MiniMax-M2.5 has an ECI of 146.7.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V, MiniMax-M2.1 and MiniMax-M2.5 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?
MiniMax-M2.1 and MiniMax-M2.5 have the largest context windows (204,800 and 204,800 tokens), against 128,000 for GLM-4.6V. Maximum output per response: GLM-4.6V up to 32,768, MiniMax-M2.1 up to 131,072, MiniMax-M2.5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; MiniMax-M2.1 accepts text; MiniMax-M2.5 accepts text. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
MiniMax-M2.5 is the newest, released Feb 12, 2026. MiniMax-M2.1 came out Dec 23, 2025; GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: GLM-4.6V 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.
