GLM-4.6V-Flash vs Qwen3 VL 30B A3B Instruct
Too close to call on our weighted score (GLM-4.6V-Flash 63, Qwen3 VL 30B A3B Instruct 62). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.6V-Flash
63/100- ECI—
- Price$0.161 / $0.559
- Context128K
Alibaba (Qwen)Qwen3 VL 30B A3B Instruct
62/100- ECI—
- Price$0.15 / $0.60
- Context262K
Add a model
Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within a point (GLM-4.6V-Flash 63/100, Qwen3 VL 30B A3B Instruct 62/100), so choose by what matters most for your work: GLM-4.6V-Flash on price and Qwen3 VL 30B A3B Instruct 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 priceGLM-4.6V-FlashGLM-4.6V-Flash $0.261 · Qwen3 VL 30B A3B Instruct $0.263 per 1M tokens (3:1 blend)
- Longest contextQwen3 VL 30B A3B InstructQwen3 VL 30B A3B Instruct 262,144 · GLM-4.6V-Flash 128,000 tokens
- Widest inputsSame inputsGLM-4.6V-Flash: Text, Images, Video · Qwen3 VL 30B A3B Instruct: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V-Flash | Qwen3 VL 30B A3B Instruct |
|---|---|---|---|
| Price | 50% | 74 | 74 |
| Inputs & features | 30% | 70 | 60 |
| Context window | 20% | 24 | 37 |
| Overall | 100% | 63/100 | 62/100 |
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-4.6V-FlashZ.ai (Zhipu) | Qwen3 VL 30B A3B InstructAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.161 | $0.15 (best) |
| Output | $0.559 (best) | $0.60 |
| Cached input | — | — |
| Blended (3:1) | $0.261 (best) | $0.263 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 13 providers |
| Limits | ||
| Context window | 128,000 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | Yes | Yes |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | OpenApache-2.0 |
| API model ID | glm-4.6v-flash | — |
| API providers | 6 | 13 (best) |
| Released | Dec 8, 2025 | Oct 4, 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.
- GLM-4.6V-Flash$2.73
- Qwen3 VL 30B A3B Instruct$2.70
Which should you choose?
Which is better: GLM-4.6V-Flash or Qwen3 VL 30B A3B Instruct?
It is close. Our weighted score puts them within a point (GLM-4.6V-Flash 63/100, Qwen3 VL 30B A3B Instruct 62/100), so choose by what matters most for your work: GLM-4.6V-Flash on price and Qwen3 VL 30B A3B Instruct 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-Flash or Qwen3 VL 30B A3B Instruct?
GLM-4.6V-Flash is cheaper at $0.161 input / $0.559 output per million tokens (median across 2 API providers; free on Z.AI). Qwen3 VL 30B A3B Instruct costs $0.15 input / $0.60 output per million tokens (median across 13 API providers). At a typical mix of three input tokens to one output token, that is $0.261 per million tokens for GLM-4.6V-Flash versus $0.263 for Qwen3 VL 30B A3B Instruct (1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GLM-4.6V-Flash has not been scored yet and Qwen3 VL 30B A3B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V-Flash and Qwen3 VL 30B A3B Instruct 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?
Qwen3 VL 30B A3B Instruct has the largest context window at 262,144 tokens, against 128,000 for GLM-4.6V-Flash. Maximum output per response: GLM-4.6V-Flash up to 32,768, Qwen3 VL 30B A3B Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V-Flash accepts text, images and video; Qwen3 VL 30B A3B Instruct accepts text, images and video. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (Apache-2.0), so you can self-host them.
Which is newer?
GLM-4.6V-Flash is the newest, released Dec 8, 2025. Qwen3 VL 30B A3B Instruct came out Oct 4, 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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