GLM-4.6V-Flash vs Qwen3 Omni 30B A3B Thinking
GLM-4.6V-Flash comes out ahead, 63 to 59 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 Omni 30B A3B Thinking
59/100- ECI—
- Price$0.25 / $0.97
- Context66K
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 Omni 30B A3B Thinking (59). It leads on price and context window. Qwen3 Omni 30B A3B Thinking wins 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 priceGLM-4.6V-FlashGLM-4.6V-Flash $0.261 · Qwen3 Omni 30B A3B Thinking $0.43 per 1M tokens (3:1 blend)
- Longest contextGLM-4.6V-FlashGLM-4.6V-Flash 128,000 · Qwen3 Omni 30B A3B Thinking 65,536 tokens
- Widest inputsQwen3 Omni 30B A3B ThinkingGLM-4.6V-Flash: Text, Images, Video · Qwen3 Omni 30B A3B Thinking: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V-Flash | Qwen3 Omni 30B A3B Thinking |
|---|---|---|---|
| Price | 50% | 74 | 64 |
| Inputs & features | 30% | 70 | 80 |
| Context window | 20% | 24 | 12 |
| Overall | 100% | 63/100 | 59/100 |
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 Omni 30B A3B ThinkingAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.161 (best) | $0.25 |
| Output | $0.559 (best) | $0.97 |
| Cached input | — | — |
| Blended (3:1) | $0.261 (best) | $0.43 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 1 providers |
| Limits | ||
| Context window | 128,000 tokens (best) | 65,536 tokens |
| Max output | 32,768 tokens (best) | 16,384 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | No |
| Audio | No | Yes |
| Video | Yes | Yes |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | OpenApache-2.0 |
| API model ID | glm-4.6v-flash | — |
| API providers | 6 (best) | 1 |
| Released | Dec 8, 2025 | Sep 22, 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 Omni 30B A3B Thinking$4.44
Which should you choose?
Which is better: GLM-4.6V-Flash or Qwen3 Omni 30B A3B Thinking?
GLM-4.6V-Flash is the better all-round choice, scoring 63/100 against Qwen3 Omni 30B A3B Thinking (59). It leads on price and context window. Qwen3 Omni 30B A3B Thinking wins 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, GLM-4.6V-Flash or Qwen3 Omni 30B A3B Thinking?
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 Omni 30B A3B Thinking costs $0.25 input / $0.97 output per million tokens (median across 1 API provider). 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 $0.43 for Qwen3 Omni 30B A3B Thinking (1.7× 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 Omni 30B A3B Thinking 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 Omni 30B A3B Thinking 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?
GLM-4.6V-Flash has the largest context window at 128,000 tokens, against 65,536 for Qwen3 Omni 30B A3B Thinking. Maximum output per response: GLM-4.6V-Flash up to 32,768, Qwen3 Omni 30B A3B Thinking up to 16,384 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V-Flash accepts text, images and video; Qwen3 Omni 30B A3B Thinking accepts text, images, audio and video. Qwen3 Omni 30B A3B Thinking handles the widest range of inputs.
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 Omni 30B A3B Thinking came out Sep 22, 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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