GLM-4.6V vs Phi-4-mini vs Qwen3 Coder Next
Too close to call on our weighted score (GLM-4.6V 59, Phi-4-mini 58, Qwen3 Coder Next 51). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
MicrosoftPhi-4-mini
58/100- ECI—
- Price$0.075 / $0.30
- Context128K
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (GLM-4.6V 59/100, Phi-4-mini 58/100, Qwen3 Coder Next 51/100), so choose by what matters most for your work: Phi-4-mini on price and Qwen3 Coder Next for long inputs. 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 pricePhi-4-miniPhi-4-mini $0.131 · GLM-4.6V $0.45 · Qwen3 Coder Next $0.45 per 1M tokens (3:1 blend)
- Longest contextQwen3 Coder NextQwen3 Coder Next 262,144 · GLM-4.6V 128,000 · Phi-4-mini 128,000 tokens
- Widest inputsGLM-4.6VGLM-4.6V: Text, Images, Video · Phi-4-mini: Text · Qwen3 Coder Next: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V | Phi-4-mini | Qwen3 Coder Next |
|---|---|---|---|---|
| Price | 50% | 66 | 92 | 66 |
| Inputs & features | 30% | 70 | 25 | 35 |
| Context window | 20% | 24 | 24 | 37 |
| Overall | 100% | 59/100 | 58/100 | 51/100 |
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 | GLM-4.6VZ.ai (Zhipu) | Phi-4-miniMicrosoft | Qwen3 Coder NextAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.075 (best) | $0.20 |
| Output | $0.90 | $0.30 (best) | $1.20 |
| Cached input | — | — | — |
| Blended (3:1) | $0.45 | $0.131 (best) | $0.45 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Azure API | Median of 11 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 4,096 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.6v | phi-4-mini | — |
| API providers | 10 | 1 | 11 (best) |
| Released | Dec 8, 2025 | Dec 11, 2024 | Feb 3, 2026 |
| Knowledge cutoff | Apr 2025 | Oct 2023 | Sep 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.6V$4.80
- Phi-4-mini$1.35
- Qwen3 Coder Next$4.40
Which should you choose?
Which is better: GLM-4.6V, Phi-4-mini or Qwen3 Coder Next?
It is close. Our weighted score puts them within a point (GLM-4.6V 59/100, Phi-4-mini 58/100, Qwen3 Coder Next 51/100), so choose by what matters most for your work: Phi-4-mini on price and Qwen3 Coder Next for long inputs. 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, GLM-4.6V, Phi-4-mini or Qwen3 Coder Next?
Phi-4-mini is cheaper at $0.075 input / $0.30 output per million tokens (official Azure API price). GLM-4.6V costs $0.30 input / $0.90 output per million tokens (official Z.AI API price); Qwen3 Coder Next costs $0.20 input / $1.20 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.131 per million tokens for Phi-4-mini versus $0.45 for GLM-4.6V (3.4× as much) and $0.45 for Qwen3 Coder Next (3.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V has not been scored yet, Phi-4-mini has not been scored yet and Qwen3 Coder Next has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V, Phi-4-mini and Qwen3 Coder Next yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3 Coder Next has the largest context window at 262,144 tokens, against 128,000 for GLM-4.6V and 128,000 for Phi-4-mini. Maximum output per response: GLM-4.6V up to 32,768, Phi-4-mini up to 4,096, Qwen3 Coder Next up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; Phi-4-mini accepts text; Qwen3 Coder Next accepts text. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Qwen3 Coder Next is the newest, released Feb 3, 2026. GLM-4.6V came out Dec 8, 2025; Phi-4-mini came out Dec 11, 2024. Knowledge cutoff: GLM-4.6V Apr 2025, Phi-4-mini Oct 2023, Qwen3 Coder Next Sep 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.
