Leaderboard / OpenBMB (MiniCPM)
MiniCPM5 2B
OpenBMB (MiniCPM) · MiniCPM5 · released 2026-09-06 · data checked 2026-11-01
MiniCPM5 2B is a 2.5B-parameter dense model from OpenBMB (MiniCPM) (China), released 2026-09-06 under the Apache-2.0 license. It scores 57.5 on the Small LM Index, #7 of 21 tiny models. It needs about 2.4 GB of memory at 4-bit with an 8K-token context.
Small LM Index
57.5
#7 of 21 scored tiny models
Likely 55.8–59.2 · from 6 of 6 core benchmarks
Parameters
2.5B
Dense
Price per 1M tokens
—
No hosted API on OpenRouter; self-host
Context window
131K
tokens
Benchmarks
Each bar shows this model's score. The tick marks the best tiny model on that benchmark. Sources: 15 reported by OpenBMB (MiniCPM). How we label sources.
τ²-Bench Lab-reported Agentic · Telecom domain only97.1
MMLU Lab-reported Knowledge · MMLU-Redux84.7
MATH-500 Lab-reported Math94.6
Specifications
- Lab
- OpenBMB (MiniCPM) (CN · China)
- Architecture
- Dense transformer
- Total parameters
- 2.5B
- Active parameters
- All (dense)
- Size class
- Tiny
- Context window
- 131K tokens
- Input modalities
- text
- Reasoning
- Hybrid (thinking on/off)
- License
- Apache-2.0
- Openness
- Open weights
- Memory to run
- ~2.4 GB at 4-bit, 8K context (more sizes)
- Hugging Face
- 1.1M downloads (30 days) · 1.8K likes
Compare with
- MiniCPM5 2B vs LFM3 1.7B (1.7B, index 60.0)
- MiniCPM5 2B vs LFM2.5 2.6B (2.7B, index 43.2)
- MiniCPM5 2B vs Qwen3.5 2B (2.3B, index 33.0)
- MiniCPM5 2B vs Granite 4.2 3B (3B, index 54.8)
- MiniCPM5 2B vs Ministral 3 3B (3B, index 49.4)
- MiniCPM5 2B vs SmolLM3 3B (3.1B, index 30.4)
Ranked in
Sources
Something wrong here? Report it.
Run it locally
Uses bartowski/MiniCPM5-2B-GGUF (Q4_K_M). Swap the tag for another size: BF16, IQ2_M, IQ3_M, IQ3_XS, IQ3_XXS, IQ4_NL, IQ4_XS, Q2_K….
Ollama
ollama run hf.co/bartowski/MiniCPM5-2B-GGUF:Q4_K_Mllama.cpp
llama-server -hf bartowski/MiniCPM5-2B-GGUF:Q4_K_MThen open http://localhost:8080 for a chat window.
vLLM (GPU server)
vllm serve openbmb/MiniCPM5-2BFull-precision weights, for a server GPU.
Memory needed: weights, context cache and runtime buffers.
| Quantization | 4K context | 32K context | 131K context |
|---|---|---|---|
| 16-bit F16 | 5.8 GB | 7.0 GB | 11 GB |
| 8-bit Q8_0 | 3.4 GB | 4.6 GB | 8.9 GB |
| 4-bit Q4_K_M | 2.3 GB | 3.5 GB | 7.7 GB |