GLM-4.7 vs MiMo-V2.5-Pro
Head-to-head specifications
| Metric | GLM-4.7 | MiMo-V2.5-Pro | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 30.0 | — |
| Coding Index | 45.3 | 60.2 | -24.8% |
| Agentic Index | 25.4 | 29.1 | — |
| Context window | 256K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.60 | $0.18 | +233.3% |
| Output speed (tokens/s) | 87 | 55 | +58.2% |
| Access | Open weights | Open weights | — |
- GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 30.0).
- MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 3.3× cheaper.
- MiMo-V2.5-Pro offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GLM-4.7 or MiMo-V2.5-Pro?
GLM-4.7 advantages
- Output speed (+37%)
MiMo-V2.5-Pro advantages
- Coding ability (+25%)
- Agentic task performance (+13%)
- Context window (+74%)
- Affordability (+70%)
Which should you choose?
- Choose the GLM-4.7 if low latency and fast generation matter for your application.
- Choose the MiMo-V2.5-Pro if coding and software development are your main workload.
Value for money
MiMo-V2.5-Pro offers more intelligence per dollar (3.3× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
GLM-4.7 vs MiMo-V2.5-Pro: which should you choose?
GLM-4.7 — Z.ai (Zhipu) text model with an Intelligence Index of 30, a 256K-token context window and a blended price of $0.6/1M tokens (open weights).
MiMo-V2.5-Pro — Xiaomi multimodal model with an Intelligence Index of 30, a 1M-token context window and a blended price of $0.18/1M tokens (open weights).
GLM-4.7 vs MiMo-V2.5-Pro: GLM-4.7 scores higher on the Intelligence Index. GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 30.0). MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 3.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-4.7 scores 30.0 versus 30.0. For software development, the Coding Index puts MiMo-V2.5-Pro ahead (60.2 vs 45.3). On agentic, multi-step tool-use tasks, MiMo-V2.5-Pro measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The MiMo-V2.5-Pro accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GLM-4.7 generates faster (87 vs 55 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiMo-V2.5-Pro is the cheaper model to run ($0.18 vs $0.60 per 1M tokens). GLM-4.7 is open weights and MiMo-V2.5-Pro is open weights. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the GLM-4.7 better than the MiMo-V2.5-Pro?
MiMo-V2.5-Pro is the clearly stronger overall choice, winning most of the dimensions that matter. GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 30.0).
What is the main difference between the GLM-4.7 and the MiMo-V2.5-Pro?
GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 30.0). MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 3.3× cheaper.
Which is better value?
MiMo-V2.5-Pro offers more intelligence per dollar (3.3× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the GLM-4.7 if low latency and fast generation matter for your application. Choose the MiMo-V2.5-Pro if coding and software development are your main workload.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.