Qwen3 VL 30B A3B Instruct vs GLM-4.6V-Flash vs Grok 4.1 Fast
Grok 4.1 Fast comes out ahead, 66 to 63 and 62 on our weighted score, though GLM-4.6V-Flash is 5% cheaper per token.
Alibaba (Qwen)Qwen3 VL 30B A3B Instruct
62/100- ECI—
- Price$0.15 / $0.60
- Context262K
Z.ai (Zhipu)GLM-4.6V-Flash
63/100- ECI—
- Price$0.161 / $0.559
- Context128K
- Our pick
xAIGrok 4.1 Fast
66/100- ECI—
- Price$0.20 / $0.50
- Context2M
Grok 4.1 Fast is our pick
Grok 4.1 Fast is the better all-round choice, scoring 66/100 against GLM-4.6V-Flash (63) and Qwen3 VL 30B A3B Instruct (62). It leads on context window. GLM-4.6V-Flash 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 VL 30B A3B Instruct $0.263 · Grok 4.1 Fast $0.275 per 1M tokens (3:1 blend)
- Longest contextGrok 4.1 FastGrok 4.1 Fast 2,000,000 · Qwen3 VL 30B A3B Instruct 262,144 · GLM-4.6V-Flash 128,000 tokens
- Widest inputsQwen3 VL 30B A3B Instruct and GLM-4.6V-FlashQwen3 VL 30B A3B Instruct: Text, Images, Video · GLM-4.6V-Flash: Text, Images, Video · Grok 4.1 Fast: Text, Images
- Self-hostingQwen3 VL 30B A3B Instruct and GLM-4.6V-FlashPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Qwen3 VL 30B A3B Instruct | GLM-4.6V-Flash | Grok 4.1 Fast |
|---|---|---|---|---|
| Price | 50% | 74 | 74 | 73 |
| Inputs & features | 30% | 60 | 70 | 50 |
| Context window | 20% | 37 | 24 | 72 |
| Overall | 100% | 62/100 | 63/100 | 66/100 |
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 | Qwen3 VL 30B A3B InstructAlibaba (Qwen) | GLM-4.6V-FlashZ.ai (Zhipu) | Grok 4.1 FastxAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.161 | $0.20 |
| Output | $0.60 | $0.559 | $0.50 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 | $0.261 (best) | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 13 providers | Median of 2 providers | Median of 6 providers |
| Limits | |||
| Context window | 262,144 tokens | 128,000 tokens | 2,000,000 tokens (best) |
| Max output | 32,768 tokens (best) | 32,768 tokens (best) | 30,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache-2.0 | Open | Proprietary |
| API model ID | — | glm-4.6v-flash | — |
| API providers | 13 (best) | 6 | 8 |
| Released | Oct 4, 2025 | Dec 8, 2025 | Nov 19, 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.
- Qwen3 VL 30B A3B Instruct$2.70
- GLM-4.6V-Flash$2.73
- Grok 4.1 Fast$3.00
Which should you choose?
Which is better: Qwen3 VL 30B A3B Instruct, GLM-4.6V-Flash or Grok 4.1 Fast?
Grok 4.1 Fast is the better all-round choice, scoring 66/100 against GLM-4.6V-Flash (63) and Qwen3 VL 30B A3B Instruct (62). It leads on context window. GLM-4.6V-Flash 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, Qwen3 VL 30B A3B Instruct, GLM-4.6V-Flash or Grok 4.1 Fast?
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); Grok 4.1 Fast costs $0.20 input / $0.50 output per million tokens (median across 6 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) and $0.275 for Grok 4.1 Fast (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3 VL 30B A3B Instruct has not been scored yet, GLM-4.6V-Flash has not been scored yet and Grok 4.1 Fast has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 VL 30B A3B Instruct, GLM-4.6V-Flash and Grok 4.1 Fast 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?
Grok 4.1 Fast has the largest context window at 2,000,000 tokens, against 262,144 for Qwen3 VL 30B A3B Instruct and 128,000 for GLM-4.6V-Flash. Maximum output per response: Qwen3 VL 30B A3B Instruct up to 32,768, GLM-4.6V-Flash up to 32,768, Grok 4.1 Fast up to 30,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3 VL 30B A3B Instruct accepts text, images and video; GLM-4.6V-Flash accepts text, images and video; Grok 4.1 Fast accepts text and images. Qwen3 VL 30B A3B Instruct handles the widest range of inputs.
Are any of these open source?
Qwen3 VL 30B A3B Instruct and GLM-4.6V-Flash publishes its weights (Apache-2.0) and can be self-hosted; Grok 4.1 Fast is proprietary.
Which is newer?
GLM-4.6V-Flash is the newest, released Dec 8, 2025. Grok 4.1 Fast came out Nov 19, 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.
