GLM-4.5 vs Pixtral Large (25.02) vs MiniMax M1 80K
MiniMax M1 80K comes out ahead, 47 to 39 and 33 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-4.5
39/100- ECI—
- Price$0.60 / $2.20
- Context131K
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
- Our pick
MiniMaxMiniMax M1 80K
47/100- ECI—
- Price$0.55 / $2.20
- Context1M
MiniMax M1 80K is our pick
MiniMax M1 80K is the better all-round choice, scoring 47/100 against GLM-4.5 (39) and Pixtral Large (25.02) (33). It leads on context window. Pixtral Large (25.02) wins on 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 priceMiniMax M1 80KMiniMax M1 80K $0.963 · GLM-4.5 $1.00 · Pixtral Large (25.02) $3.00 per 1M tokens (3:1 blend)
- Longest contextMiniMax M1 80KMiniMax M1 80K 1,000,000 · GLM-4.5 131,072 · Pixtral Large (25.02) 128,000 tokens
- Widest inputsPixtral Large (25.02)GLM-4.5: Text · Pixtral Large (25.02): Text, Images · MiniMax M1 80K: Text
- Self-hostingGLM-4.5 and MiniMax M1 80KPublishes downloadable weights (Apache-2.0)
| Measure | Weight | GLM-4.5 | Pixtral Large (25.02) | MiniMax M1 80K |
|---|---|---|---|---|
| Price | 50% | 48 | 26 | 48 |
| Inputs & features | 30% | 35 | 50 | 35 |
| Context window | 20% | 25 | 24 | 60 |
| Overall | 100% | 39/100 | 33/100 | 47/100 |
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-4.5Z.ai (Zhipu) | Pixtral Large (25.02)Mistral AI | MiniMax M1 80KMiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $2.00 | $0.55 (best) |
| Output | $2.20 (best) | $6.00 | $2.20 (best) |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $3.00 | $0.963 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 3 providers | Median of 5 providers |
| Limits | |||
| Context window | 131,072 tokens | 128,000 tokens | 1,000,000 tokens (best) |
| Max output | 98,304 tokens (best) | 8,192 tokens | 80,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenApache-2.0 |
| API model ID | glm-4.5 | — | — |
| API providers | 19 (best) | 3 | 5 |
| Released | Jul 28, 2025 | Apr 8, 2025 | Jun 17, 2025 |
| 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-4.5$10.40
- Pixtral Large (25.02)$32.00
- MiniMax M1 80K$9.90
Which should you choose?
Which is better: GLM-4.5, Pixtral Large (25.02) or MiniMax M1 80K?
MiniMax M1 80K is the better all-round choice, scoring 47/100 against GLM-4.5 (39) and Pixtral Large (25.02) (33). It leads on context window. Pixtral Large (25.02) wins on 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-4.5, Pixtral Large (25.02) or MiniMax M1 80K?
MiniMax M1 80K is cheaper at $0.55 input / $2.20 output per million tokens (median across 5 API providers). GLM-4.5 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price); Pixtral Large (25.02) costs $2.00 input / $6.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.963 per million tokens for MiniMax M1 80K versus $1.00 for GLM-4.5 (1× as much) and $3.00 for Pixtral Large (25.02) (3.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5 has not been scored yet, Pixtral Large (25.02) has not been scored yet and MiniMax M1 80K has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5, Pixtral Large (25.02) and MiniMax M1 80K 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 M1 80K has the largest context window at 1,000,000 tokens, against 131,072 for GLM-4.5 and 128,000 for Pixtral Large (25.02). Maximum output per response: GLM-4.5 up to 98,304, Pixtral Large (25.02) up to 8,192, MiniMax M1 80K up to 80,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5 accepts text; Pixtral Large (25.02) accepts text and images; MiniMax M1 80K accepts text. Pixtral Large (25.02) handles the widest range of inputs.
Are any of these open source?
GLM-4.5 and MiniMax M1 80K publishes its weights (Apache-2.0) and can be self-hosted; Pixtral Large (25.02) is proprietary.
Which is newer?
GLM-4.5 is the newest, released Jul 28, 2025. MiniMax M1 80K came out Jun 17, 2025; Pixtral Large (25.02) came out Apr 8, 2025. Knowledge cutoff: GLM-4.5 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 Nov 2, 2026.
