MiniMax-M2.5 vs GLM-4.6V vs GPT-5.4 nano
GPT-5.4 nano comes out ahead, 63 to 59 and 48 on our weighted score.
MiniMaxMiniMax-M2.5
48/100- ECI146.7
- Price$0.30 / $1.20
- Context205K
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
- Our pick
OpenAIGPT-5.4 nano
63/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
GPT-5.4 nano is our pick
GPT-5.4 nano is the better all-round choice, scoring 63/100 against GLM-4.6V (59) and MiniMax-M2.5 (48). 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 priceGLM-4.6VGLM-4.6V $0.45 · GPT-5.4 nano $0.463 · MiniMax-M2.5 $0.525 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · MiniMax-M2.5 204,800 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VMiniMax-M2.5: Text · GLM-4.6V: Text, Images, Video · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.5 and GLM-4.6VPublishes downloadable weights
| Measure | Weight | MiniMax-M2.5 | GLM-4.6V | GPT-5.4 nano |
|---|---|---|---|---|
| Price | 50% | 63 | 66 | 66 |
| Inputs & features | 30% | 35 | 70 | 70 |
| Context window | 20% | 32 | 24 | 44 |
| Overall | 100% | 48/100 | 59/100 | 63/100 |
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 | MiniMax-M2.5MiniMax | GLM-4.6VZ.ai (Zhipu) | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.7 (best) | — | 145.8 |
| ECI rank | #66 of 148 (best) | — | #75 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 78.5% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 44.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 87.8% |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.30 | $0.30 | $0.20 (best) |
| Output | $1.20 | $0.90 (best) | $1.25 |
| Cached input | $0.03 | — | $0.02 (best) |
| Blended (3:1) | $0.525 | $0.45 (best) | $0.463 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Official Z.AI API | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 128,000 tokens | 400,000 tokens (best) |
| Max output | 131,072 tokens (best) | 32,768 tokens | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M2.5 | glm-4.6v | gpt-5.4-nano |
| API providers | 21 | 10 | 26 (best) |
| Released | Feb 12, 2026 | Dec 8, 2025 | Mar 17, 2026 |
| Knowledge cutoff | — | Apr 2025 | Aug 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiniMax-M2.5$5.40
- GLM-4.6V$4.80
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.5, GLM-4.6V or GPT-5.4 nano?
GPT-5.4 nano is the better all-round choice, scoring 63/100 against GLM-4.6V (59) and MiniMax-M2.5 (48). 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, MiniMax-M2.5, GLM-4.6V or GPT-5.4 nano?
GLM-4.6V is cheaper at $0.30 input / $0.90 output per million tokens (official Z.AI API price). GPT-5.4 nano costs $0.20 input / $1.25 output per million tokens (official OpenAI 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 $0.463 for GPT-5.4 nano (1× 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. MiniMax-M2.5 has an ECI of 146.7, GLM-4.6V has not been scored yet and GPT-5.4 nano has an ECI of 145.8.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.5, GLM-4.6V and GPT-5.4 nano 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?
GPT-5.4 nano has the largest context window at 400,000 tokens, against 204,800 for MiniMax-M2.5 and 128,000 for GLM-4.6V. Maximum output per response: MiniMax-M2.5 up to 131,072, GLM-4.6V up to 32,768, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.5 accepts text; GLM-4.6V accepts text, images and video; GPT-5.4 nano accepts text and images. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.5 and GLM-4.6V publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
GPT-5.4 nano is the newest, released Mar 17, 2026. MiniMax-M2.5 came out Feb 12, 2026; GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: GLM-4.6V Apr 2025, GPT-5.4 nano Aug 31, 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.
