GLM-4.6V-Flash vs Qwen3 VL 235B A22B Instruct
GLM-4.6V-Flash comes out ahead, 63 to 49 on our weighted score, and it is the cheaper option too.
- Our pick
Z.ai (Zhipu)GLM-4.6V-Flash
63/100- ECI—
- Price$0.161 / $0.559
- Context128K
Alibaba (Qwen)Qwen3 VL 235B A22B Instruct
49/100- ECI—
- Price$0.30 / $1.55
- Context131K
Add a model
Make it a three-way comparison.
GLM-4.6V-Flash is our pick
GLM-4.6V-Flash is the better all-round choice, scoring 63/100 against Qwen3 VL 235B A22B Instruct (49). It leads on price and 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 priceGLM-4.6V-FlashGLM-4.6V-Flash $0.261 · Qwen3 VL 235B A22B Instruct $0.613 per 1M tokens (3:1 blend)
- Longest contextQwen3 VL 235B A22B InstructQwen3 VL 235B A22B Instruct 131,072 · GLM-4.6V-Flash 128,000 tokens
- Widest inputsGLM-4.6V-FlashGLM-4.6V-Flash: Text, Images, Video · Qwen3 VL 235B A22B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V-Flash | Qwen3 VL 235B A22B Instruct |
|---|---|---|---|
| Price | 50% | 74 | 57 |
| Inputs & features | 30% | 70 | 50 |
| Context window | 20% | 24 | 25 |
| Overall | 100% | 63/100 | 49/100 |
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 235B A22B InstructAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.161 (best) | $0.30 |
| Output | $0.559 (best) | $1.55 |
| Cached input | — | — |
| Blended (3:1) | $0.261 (best) | $0.613 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 16 providers |
| Limits | ||
| Context window | 128,000 tokens | 131,072 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 | No |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | glm-4.6v-flash | — |
| API providers | 6 | 16 (best) |
| Released | Dec 8, 2025 | Sep 23, 2025 |
| Knowledge cutoff | — | Mar 31, 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-Flash$2.73
- Qwen3 VL 235B A22B Instruct$6.10
Which should you choose?
Which is better: GLM-4.6V-Flash or Qwen3 VL 235B A22B Instruct?
GLM-4.6V-Flash is the better all-round choice, scoring 63/100 against Qwen3 VL 235B A22B Instruct (49). It leads on price and 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, GLM-4.6V-Flash or Qwen3 VL 235B A22B 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 235B A22B Instruct costs $0.30 input / $1.55 output per million tokens (median across 16 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 $0.613 for Qwen3 VL 235B A22B Instruct (2.4× 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 235B A22B 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 235B A22B 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 235B A22B Instruct has the largest context window at 131,072 tokens, against 128,000 for GLM-4.6V-Flash. Maximum output per response: GLM-4.6V-Flash up to 32,768, Qwen3 VL 235B A22B 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 235B A22B Instruct accepts text and images. GLM-4.6V-Flash handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
GLM-4.6V-Flash is the newest, released Dec 8, 2025. Qwen3 VL 235B A22B Instruct came out Sep 23, 2025. Knowledge cutoff: Qwen3 VL 235B A22B Instruct Mar 31, 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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