ALLaM-2-7b vs GLM-4.5V vs Qwen2.5-VL 72B Instruct
GLM-4.5V comes out ahead, 47 to 40 and 0 on our weighted score, and it is the cheaper option too.
SDAIA
ALLaM-2-7b
0/100- ECI—
- Price—
- Context4K
- Our pick
Z.ai (Zhipu)GLM-4.5V
47/100- ECI—
- Price$0.60 / $1.80
- Context64K
Alibaba (Qwen)Qwen2.5-VL 72B Instruct
40/100- ECI—
- Price$0.905 / $1.00
- Context131K
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 47/100 against Qwen2.5-VL 72B Instruct (40) and ALLaM-2-7b (0). It leads on inputs & features. Qwen2.5-VL 72B Instruct wins on context window. The score weighs inputs & features 60%, context window 40%. 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-4.5VGLM-4.5V $0.90 · Qwen2.5-VL 72B Instruct $0.93 per 1M tokens (3:1 blend) · ALLaM-2-7b unpriced
- Longest contextQwen2.5-VL 72B InstructQwen2.5-VL 72B Instruct 131,072 · GLM-4.5V 64,000 · ALLaM-2-7b 4,096 tokens
- Widest inputsGLM-4.5VALLaM-2-7b: Text · GLM-4.5V: Text, Images, Video · Qwen2.5-VL 72B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | ALLaM-2-7b | GLM-4.5V | Qwen2.5-VL 72B Instruct |
|---|---|---|---|---|
| Inputs & features | 60% | 0 | 70 | 50 |
| Context window | 40% | 0 | 12 | 25 |
| Overall | 100% | 0/100 | 47/100 | 40/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | ALLaM-2-7bSDAIA | GLM-4.5VZ.ai (Zhipu) | Qwen2.5-VL 72B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $0.60 (best) | $0.905 |
| Output | — | $1.80 | $1.00 (best) |
| Cached input | — | $0.11 | — |
| Blended (3:1) | — | $0.90 (best) | $0.93 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official Z.AI API | Median of 6 providers |
| Limits | |||
| Context window | 4,096 tokens | 64,000 tokens | 131,072 tokens (best) |
| Max output | 4,096 tokens | 16,384 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | glm-4.5v | — |
| API providers | 2 | 17 (best) | 6 |
| Released | Jan 23, 2025 | Aug 11, 2025 | Jan 26, 2025 |
| Knowledge cutoff | — | Apr 2025 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- ALLaM-2-7b—
- GLM-4.5V$9.60
- Qwen2.5-VL 72B Instruct$11.06
Which should you choose?
Which is better: ALLaM-2-7b, GLM-4.5V or Qwen2.5-VL 72B Instruct?
GLM-4.5V is the better all-round choice, scoring 47/100 against Qwen2.5-VL 72B Instruct (40) and ALLaM-2-7b (0). It leads on inputs & features. Qwen2.5-VL 72B Instruct wins on context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, ALLaM-2-7b, GLM-4.5V or Qwen2.5-VL 72B Instruct?
GLM-4.5V is cheaper at $0.60 input / $1.80 output per million tokens (official Z.AI API price). Qwen2.5-VL 72B Instruct costs $0.905 input / $1.00 output per million tokens (median across 6 API providers). At a typical mix of three input tokens to one output token, that is $0.90 per million tokens for GLM-4.5V versus $0.93 for Qwen2.5-VL 72B Instruct (1× as much). ALLaM-2-7b has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. ALLaM-2-7b has not been scored yet, GLM-4.5V has not been scored yet and Qwen2.5-VL 72B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for ALLaM-2-7b, GLM-4.5V and Qwen2.5-VL 72B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that ALLaM-2-7b does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen2.5-VL 72B Instruct has the largest context window at 131,072 tokens, against 64,000 for GLM-4.5V and 4,096 for ALLaM-2-7b. Maximum output per response: ALLaM-2-7b up to 4,096, GLM-4.5V up to 16,384, Qwen2.5-VL 72B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
ALLaM-2-7b accepts text; GLM-4.5V accepts text, images and video; Qwen2.5-VL 72B Instruct accepts text and images. GLM-4.5V 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?
GLM-4.5V is the newest, released Aug 11, 2025. Qwen2.5-VL 72B Instruct came out Jan 26, 2025; ALLaM-2-7b came out Jan 23, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Qwen2.5-VL 72B Instruct Apr 2024.
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.
