MiniCPM5-1B vs MAI-Code-1-Flash
MAI-Code-1-Flash comes out ahead, 41 to 31 on our weighted score.
OpenBMB
MiniCPM5-1B
31/100- ECI—
- Price—
- Context131K
- Our pick
MicrosoftMAI-Code-1-Flash
41/100- ECI—
- Price—
- Context256K
Add a model
Make it a three-way comparison.
MAI-Code-1-Flash is our pick
MAI-Code-1-Flash is the better all-round choice, scoring 41/100 against MiniCPM5-1B (31). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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
- Longest contextMAI-Code-1-FlashMAI-Code-1-Flash 256,000 · MiniCPM5-1B 131,072 tokens
- Widest inputsSame inputsMiniCPM5-1B: Text · MAI-Code-1-Flash: Text
- Self-hostingMiniCPM5-1BPublishes downloadable weights (apache-2.0)
| Measure | Weight | MiniCPM5-1B | MAI-Code-1-Flash |
|---|---|---|---|
| Inputs & features | 60% | 35 | 45 |
| Context window | 40% | 24 | 36 |
| Overall | 100% | 31/100 | 41/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniCPM5-1BOpenBMB | MAI-Code-1-FlashMicrosoft |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | — |
| Output | — | — |
| Cached input | — | — |
| Blended (3:1) | — | — |
| Long-context rate | — | — |
| Price source | — | — |
| Limits | ||
| Context window | 131,072 tokens | 256,000 tokens (best) |
| Max output | 131,072 tokens (best) | 128,000 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | Openapache-2.0 | Proprietary |
| API model ID | — | — |
| API providers | — | — |
| Released | May 19, 2026 | Jun 2, 2026 |
| Knowledge cutoff | — | Dec 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiniCPM5-1B—
- MAI-Code-1-Flash—
Which should you choose?
Which is better: MiniCPM5-1B or MAI-Code-1-Flash?
MAI-Code-1-Flash is the better all-round choice, scoring 41/100 against MiniCPM5-1B (31). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, MiniCPM5-1B or MAI-Code-1-Flash?
None of these models has a published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. MiniCPM5-1B has not been scored yet and MAI-Code-1-Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiniCPM5-1B and MAI-Code-1-Flash yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
MAI-Code-1-Flash has the largest context window at 256,000 tokens, against 131,072 for MiniCPM5-1B. Maximum output per response: MiniCPM5-1B up to 131,072, MAI-Code-1-Flash up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniCPM5-1B accepts text; MAI-Code-1-Flash accepts text. They handle the same number of input types.
Are any of these open source?
MiniCPM5-1B publishes its weights (apache-2.0) and can be self-hosted; MAI-Code-1-Flash is proprietary.
Which is newer?
MAI-Code-1-Flash is the newest, released Jun 2, 2026. MiniCPM5-1B came out May 19, 2026. Knowledge cutoff: MAI-Code-1-Flash Dec 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.
