AutoGLM Phone 9B Multilingual vs Gemma 4 E2B IT vs Trinity Mini
Gemma 4 E2B IT comes out ahead, 76 to 65 and 60 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)AutoGLM Phone 9B Multilingual
60/100- ECI—
- Price$0.035 / $0.138
- Context66K
- Our pick
GoogleGemma 4 E2B IT
76/100- ECI—
- Price$0.04 / $0.08
- Context131K
Arcee AITrinity Mini
65/100- ECI—
- Price$0.045 / $0.15
- Context131K
Gemma 4 E2B IT is our pick
Gemma 4 E2B IT is the better all-round choice, scoring 76/100 against Trinity Mini (65) and AutoGLM Phone 9B Multilingual (60). It leads 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 priceGemma 4 E2B ITGemma 4 E2B IT $0.05 · AutoGLM Phone 9B Multilingual $0.061 · Trinity Mini $0.071 per 1M tokens (3:1 blend)
- Longest contextGemma 4 E2B IT and Trinity MiniGemma 4 E2B IT 131,072 · Trinity Mini 131,072 · AutoGLM Phone 9B Multilingual 65,536 tokens
- Widest inputsGemma 4 E2B ITAutoGLM Phone 9B Multilingual: Text, Images · Gemma 4 E2B IT: Text, Images, Audio · Trinity Mini: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | AutoGLM Phone 9B Multilingual | Gemma 4 E2B IT | Trinity Mini |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 100 |
| Inputs & features | 30% | 25 | 70 | 35 |
| Context window | 20% | 12 | 25 | 25 |
| Overall | 100% | 60/100 | 76/100 | 65/100 |
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 | AutoGLM Phone 9B MultilingualZ.ai (Zhipu) | Gemma 4 E2B ITGoogle | Trinity MiniArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.035 (best) | $0.04 | $0.045 |
| Output | $0.138 | $0.08 (best) | $0.15 |
| Cached input | — | — | — |
| Blended (3:1) | $0.061 | $0.05 (best) | $0.071 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 1 providers | Median of 1 providers |
| Limits | |||
| Context window | 65,536 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 65,536 tokens | 8,192 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenMIT | Open | OpenOpenMDW-1.1 |
| API model ID | — | — | — |
| API providers | 2 (best) | 1 | 1 |
| Released | Dec 10, 2025 | Apr 2, 2026 | Dec 1, 2025 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- AutoGLM Phone 9B Multilingual$0.626
- Gemma 4 E2B IT$0.56
- Trinity Mini$0.75
Which should you choose?
Which is better: AutoGLM Phone 9B Multilingual, Gemma 4 E2B IT or Trinity Mini?
Gemma 4 E2B IT is the better all-round choice, scoring 76/100 against Trinity Mini (65) and AutoGLM Phone 9B Multilingual (60). It leads 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, AutoGLM Phone 9B Multilingual, Gemma 4 E2B IT or Trinity Mini?
Gemma 4 E2B IT is cheaper at $0.04 input / $0.08 output per million tokens (median across 1 API provider). AutoGLM Phone 9B Multilingual costs $0.035 input / $0.138 output per million tokens (median across 2 API providers); Trinity Mini costs $0.045 input / $0.15 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.05 per million tokens for Gemma 4 E2B IT versus $0.061 for AutoGLM Phone 9B Multilingual (1.2× as much) and $0.071 for Trinity Mini (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. AutoGLM Phone 9B Multilingual has not been scored yet, Gemma 4 E2B IT has not been scored yet and Trinity Mini has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for AutoGLM Phone 9B Multilingual, Gemma 4 E2B IT and Trinity Mini yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that AutoGLM Phone 9B Multilingual does not support tool calling, which most coding agents need.
Which has the bigger context window?
Gemma 4 E2B IT and Trinity Mini have the largest context windows (131,072 and 131,072 tokens), against 65,536 for AutoGLM Phone 9B Multilingual. Maximum output per response: AutoGLM Phone 9B Multilingual up to 65,536, Gemma 4 E2B IT up to 8,192, Trinity Mini up to 131,072 tokens.
Which can read images, PDFs, audio or video?
AutoGLM Phone 9B Multilingual accepts text and images; Gemma 4 E2B IT accepts text, images and audio; Trinity Mini accepts text. Gemma 4 E2B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (MIT and OpenMDW-1.1), so you can self-host them.
Which is newer?
Gemma 4 E2B IT is the newest, released Apr 2, 2026. AutoGLM Phone 9B Multilingual came out Dec 10, 2025; Trinity Mini came out Dec 1, 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.
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