GLM-4.5-Flash vs Qwen2.5-VL 7B Instruct
GLM-4.5-Flash comes out ahead, 65 to 51 on our weighted score, and it is the cheaper option too.
- Our pick
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
Alibaba (Qwen)Qwen2.5-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
Add a model
Make it a three-way comparison.
GLM-4.5-Flash is our pick
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Qwen2.5-VL 7B Instruct (51). It leads on price. Qwen2.5-VL 7B Instruct 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-4.5-FlashGLM-4.5-Flash Free · Qwen2.5-VL 7B Instruct $0.525 per 1M tokens (3:1 blend)
- Longest contextAbout the sameGLM-4.5-Flash 131,072 · Qwen2.5-VL 7B Instruct 131,072 tokens
- Widest inputsQwen2.5-VL 7B InstructGLM-4.5-Flash: Text · Qwen2.5-VL 7B Instruct: Text, Images
- Self-hostingQwen2.5-VL 7B InstructPublishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Qwen2.5-VL 7B Instruct |
|---|---|---|---|
| Price | 50% | 100 | 63 |
| Inputs & features | 30% | 35 | 50 |
| Context window | 20% | 24 | 24 |
| Overall | 100% | 65/100 | 51/100 |
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-4.5-FlashZ.ai (Zhipu) | Qwen2.5-VL 7B InstructAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | Free (best) | $0.35 |
| Output | Free (best) | $1.05 |
| Cached input | — | — |
| Blended (3:1) | Free (best) | $0.525 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API |
| Limits | ||
| Context window | 131,072 tokens | 131,072 tokens |
| Max output | 98,304 tokens (best) | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | glm-4.5-flash | qwen2-5-vl-7b-instruct |
| API providers | 4 (best) | 1 |
| Released | Jul 28, 2025 | Sep 2024 |
| 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.
- GLM-4.5-FlashFree
- Qwen2.5-VL 7B Instruct$5.60
Which should you choose?
Which is better: GLM-4.5-Flash or Qwen2.5-VL 7B Instruct?
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Qwen2.5-VL 7B Instruct (51). It leads on price. Qwen2.5-VL 7B Instruct 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-4.5-Flash or Qwen2.5-VL 7B Instruct?
GLM-4.5-Flash is cheaper at Free input / Free output per million tokens (official Z.AI API price). Qwen2.5-VL 7B Instruct costs $0.35 input / $1.05 output per million tokens (official Alibaba API price). GLM-4.5-Flash is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GLM-4.5-Flash has not been scored yet and Qwen2.5-VL 7B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash and Qwen2.5-VL 7B Instruct 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-4.5-Flash and Qwen2.5-VL 7B Instruct share the same 131,072-token context window. Maximum output per response: GLM-4.5-Flash up to 98,304, Qwen2.5-VL 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Qwen2.5-VL 7B Instruct accepts text and images. Qwen2.5-VL 7B Instruct handles the widest range of inputs.
Are any of these open source?
Qwen2.5-VL 7B Instruct publishes its weights and can be self-hosted; GLM-4.5-Flash is proprietary.
Which is newer?
GLM-4.5-Flash is the newest, released Jul 28, 2025. Qwen2.5-VL 7B Instruct came out Sep 2024. Knowledge cutoff: GLM-4.5-Flash Apr 2025, Qwen2.5-VL 7B 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 Oct 4, 2026.
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