GLM-4.6V-Flash vs Grok 4.1 Fast vs Qwen3 VL 235B A22B Instruct
Grok 4.1 Fast comes out ahead, 66 to 63 and 49 on our weighted score, though GLM-4.6V-Flash is 5% cheaper per token.
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
Alibaba (Qwen)Qwen3 VL 235B A22B Instruct
49/100- ECI—
- Price$0.30 / $1.55
- Context131K
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 235B A22B Instruct (49). 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 · Grok 4.1 Fast $0.275 · Qwen3 VL 235B A22B Instruct $0.613 per 1M tokens (3:1 blend)
- Longest contextGrok 4.1 FastGrok 4.1 Fast 2,000,000 · Qwen3 VL 235B A22B Instruct 131,072 · GLM-4.6V-Flash 128,000 tokens
- Widest inputsGLM-4.6V-FlashGLM-4.6V-Flash: Text, Images, Video · Grok 4.1 Fast: Text, Images · Qwen3 VL 235B A22B Instruct: Text, Images
- Self-hostingGLM-4.6V-Flash and Qwen3 VL 235B A22B InstructPublishes downloadable weights
| Measure | Weight | GLM-4.6V-Flash | Grok 4.1 Fast | Qwen3 VL 235B A22B Instruct |
|---|---|---|---|---|
| Price | 50% | 74 | 73 | 57 |
| Inputs & features | 30% | 70 | 50 | 50 |
| Context window | 20% | 24 | 72 | 25 |
| Overall | 100% | 63/100 | 66/100 | 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) | Grok 4.1 FastxAI | Qwen3 VL 235B A22B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.161 (best) | $0.20 | $0.30 |
| Output | $0.559 | $0.50 (best) | $1.55 |
| Cached input | — | — | — |
| Blended (3:1) | $0.261 (best) | $0.275 | $0.613 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 6 providers | Median of 16 providers |
| Limits | |||
| Context window | 128,000 tokens | 2,000,000 tokens (best) | 131,072 tokens |
| Max output | 32,768 tokens (best) | 30,000 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| 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 | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-4.6v-flash | — | — |
| API providers | 6 | 8 | 16 (best) |
| Released | Dec 8, 2025 | Nov 19, 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
- Grok 4.1 Fast$3.00
- Qwen3 VL 235B A22B Instruct$6.10
Which should you choose?
Which is better: GLM-4.6V-Flash, Grok 4.1 Fast or Qwen3 VL 235B A22B Instruct?
Grok 4.1 Fast is the better all-round choice, scoring 66/100 against GLM-4.6V-Flash (63) and Qwen3 VL 235B A22B Instruct (49). 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, GLM-4.6V-Flash, Grok 4.1 Fast 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). Grok 4.1 Fast costs $0.20 input / $0.50 output per million tokens (median across 6 API providers); 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 $0.275 for Grok 4.1 Fast (1.1× as much) and $0.613 for Qwen3 VL 235B A22B Instruct (2.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V-Flash has not been scored yet, Grok 4.1 Fast 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, Grok 4.1 Fast 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. 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 131,072 for Qwen3 VL 235B A22B Instruct and 128,000 for GLM-4.6V-Flash. Maximum output per response: GLM-4.6V-Flash up to 32,768, Grok 4.1 Fast up to 30,000, 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; Grok 4.1 Fast accepts text and images; 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?
GLM-4.6V-Flash and Qwen3 VL 235B A22B Instruct publishes its weights 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 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.
