GLM-4.5 vs Ling-1T vs Qwen3 VL 235B A22B Thinking
Qwen3 VL 235B A22B Thinking comes out ahead, 48 to 40 and 37 on our weighted score, though GLM-4.5 is 23% cheaper per token.
Z.ai (Zhipu)GLM-4.5
40/100- ECI—
- Price$0.60 / $2.20
- Context131K
inclusionAILing-1T
37/100- ECI—
- Price$0.57 / $2.29
- Context128K
- Our pick
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
48/100- ECI—
- Price$0.40 / $4.00
- Context131K
Qwen3 VL 235B A22B Thinking is our pick
Qwen3 VL 235B A22B Thinking is the better all-round choice, scoring 48/100 against GLM-4.5 (40) and Ling-1T (37). It leads 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-4.5 and Ling-1TGLM-4.5 $1.00 · Ling-1T $1.00 · Qwen3 VL 235B A22B Thinking $1.30 per 1M tokens (3:1 blend)
- Longest contextGLM-4.5 and Qwen3 VL 235B A22B ThinkingGLM-4.5 131,072 · Qwen3 VL 235B A22B Thinking 131,072 · Ling-1T 128,000 tokens
- Widest inputsQwen3 VL 235B A22B ThinkingGLM-4.5: Text · Ling-1T: Text · Qwen3 VL 235B A22B Thinking: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.5 | Ling-1T | Qwen3 VL 235B A22B Thinking |
|---|---|---|---|---|
| Price | 50% | 50 | 50 | 44 |
| Inputs & features | 30% | 35 | 25 | 70 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 40/100 | 37/100 | 48/100 |
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-4.5Z.ai (Zhipu) | Ling-1TinclusionAI | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.57 | $0.40 (best) |
| Output | $2.20 (best) | $2.29 | $4.00 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 (best) | $1.00 (best) | $1.30 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Bailing API | Median of 9 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 98,304 tokens (best) | 32,000 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.5 | Ling-1T | — |
| API providers | 14 (best) | 1 | 9 |
| Released | Jul 28, 2025 | Oct 2025 | Sep 23, 2025 |
| Knowledge cutoff | Apr 2025 | Jun 2024 | 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-4.5$10.40
- Ling-1T$10.28
- Qwen3 VL 235B A22B Thinking$12.00
Which should you choose?
Which is better: GLM-4.5, Ling-1T or Qwen3 VL 235B A22B Thinking?
Qwen3 VL 235B A22B Thinking is the better all-round choice, scoring 48/100 against GLM-4.5 (40) and Ling-1T (37). It leads 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-4.5, Ling-1T or Qwen3 VL 235B A22B Thinking?
GLM-4.5 is cheaper at $0.60 input / $2.20 output per million tokens (official Z.AI API price). Ling-1T costs $0.57 input / $2.29 output per million tokens (official Bailing API price); Qwen3 VL 235B A22B Thinking costs $0.40 input / $4.00 output per million tokens (median across 9 API providers). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for GLM-4.5 versus $1.00 for Ling-1T (1× as much) and $1.30 for Qwen3 VL 235B A22B Thinking (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5 has not been scored yet, Ling-1T has not been scored yet and Qwen3 VL 235B A22B Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5, Ling-1T and Qwen3 VL 235B A22B Thinking 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?
GLM-4.5 and Qwen3 VL 235B A22B Thinking have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Ling-1T. Maximum output per response: GLM-4.5 up to 98,304, Ling-1T up to 32,000, Qwen3 VL 235B A22B Thinking up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5 accepts text; Ling-1T accepts text; Qwen3 VL 235B A22B Thinking accepts text and images. Qwen3 VL 235B A22B Thinking handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Ling-1T is the newest, released Oct 2025. Qwen3 VL 235B A22B Thinking came out Sep 23, 2025; GLM-4.5 came out Jul 28, 2025. Knowledge cutoff: GLM-4.5 Apr 2025, Ling-1T Jun 2024, Qwen3 VL 235B A22B Thinking 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 Oct 4, 2026.
