AutoGLM Phone 9B Multilingual vs Qwen3.5 4B vs Trinity Mini
Qwen3.5 4B comes out ahead, 78 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
Alibaba (Qwen)Qwen3.5 4B
78/100- ECI—
- Price$0.04 / $0.07
- Context262K
Arcee AITrinity Mini
65/100- ECI—
- Price$0.045 / $0.15
- Context131K
Qwen3.5 4B is our pick
Qwen3.5 4B is the better all-round choice, scoring 78/100 against Trinity Mini (65) and AutoGLM Phone 9B Multilingual (60). It leads on inputs & features and context window. 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 priceQwen3.5 4BQwen3.5 4B $0.048 · AutoGLM Phone 9B Multilingual $0.061 · Trinity Mini $0.071 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 4BQwen3.5 4B 262,144 · Trinity Mini 131,072 · AutoGLM Phone 9B Multilingual 65,536 tokens
- Widest inputsQwen3.5 4BAutoGLM Phone 9B Multilingual: Text, Images · Qwen3.5 4B: Text, Images, Video · Trinity Mini: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | AutoGLM Phone 9B Multilingual | Qwen3.5 4B | Trinity Mini |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 100 |
| Inputs & features | 30% | 25 | 70 | 35 |
| Context window | 20% | 12 | 37 | 25 |
| Overall | 100% | 60/100 | 78/100 | 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) | Qwen3.5 4BAlibaba (Qwen) | Trinity MiniArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 55.8% | — |
| Price per million tokens | |||
| Input | $0.035 (best) | $0.04 | $0.045 |
| Output | $0.138 | $0.07 (best) | $0.15 |
| Cached input | — | — | — |
| Blended (3:1) | $0.061 | $0.048 (best) | $0.071 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 3 providers | Median of 1 providers |
| Limits | |||
| Context window | 65,536 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 65,536 tokens | 81,920 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenMIT | OpenApache-2.0 | OpenOpenMDW-1.1 |
| API model ID | — | — | — |
| API providers | 2 | 5 (best) | 1 |
| Released | Dec 10, 2025 | Mar 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
- Qwen3.5 4B$0.54
- Trinity Mini$0.75
Which should you choose?
Which is better: AutoGLM Phone 9B Multilingual, Qwen3.5 4B or Trinity Mini?
Qwen3.5 4B is the better all-round choice, scoring 78/100 against Trinity Mini (65) and AutoGLM Phone 9B Multilingual (60). It leads on inputs & features and context window. 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, Qwen3.5 4B or Trinity Mini?
Qwen3.5 4B is cheaper at $0.04 input / $0.07 output per million tokens (median across 3 API providers). 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 $0.048 per million tokens for Qwen3.5 4B versus $0.061 for AutoGLM Phone 9B Multilingual (1.3× as much) and $0.071 for Trinity Mini (1.5× 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, Qwen3.5 4B 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, Qwen3.5 4B 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?
Qwen3.5 4B has the largest context window at 262,144 tokens, against 131,072 for Trinity Mini and 65,536 for AutoGLM Phone 9B Multilingual. Maximum output per response: AutoGLM Phone 9B Multilingual up to 65,536, Qwen3.5 4B up to 81,920, Trinity Mini up to 131,072 tokens.
Which can read images, PDFs, audio or video?
AutoGLM Phone 9B Multilingual accepts text and images; Qwen3.5 4B accepts text, images and video; Trinity Mini accepts text. Qwen3.5 4B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (MIT, Apache-2.0 and OpenMDW-1.1), so you can self-host them.
Which is newer?
Qwen3.5 4B is the newest, released Mar 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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