Qwen2.5-VL 7B Instruct vs Qwen2.5 14B Instruct
Qwen2.5-VL 7B Instruct comes out ahead, 51 to 42 on our weighted score, and it is the cheaper option too.
- Our pick
Alibaba (Qwen)Qwen2.5-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
- Alibaba (Qwen)
Qwen2.5 14B Instruct
42/100- ECI—
- Price$0.35 / $1.40
- Context131K
Add a model
Make it a three-way comparison.
Qwen2.5-VL 7B Instruct is our pick
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen2.5 14B Instruct (42). It leads on price and 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 priceQwen2.5-VL 7B InstructQwen2.5-VL 7B Instruct $0.525 · Qwen2.5 14B Instruct $0.613 per 1M tokens (3:1 blend)
- Longest contextAbout the sameQwen2.5-VL 7B Instruct 131,072 · Qwen2.5 14B Instruct 131,072 tokens
- Widest inputsQwen2.5-VL 7B InstructQwen2.5-VL 7B Instruct: Text, Images · Qwen2.5 14B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5-VL 7B Instruct | Qwen2.5 14B Instruct |
|---|---|---|---|
| Price | 50% | 63 | 60 |
| Inputs & features | 30% | 50 | 25 |
| Context window | 20% | 24 | 24 |
| Overall | 100% | 51/100 | 42/100 |
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 | Qwen2.5-VL 7B InstructAlibaba (Qwen) | Qwen2.5 14B InstructAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.35 | $0.35 |
| Output | $1.05 (best) | $1.40 |
| Cached input | — | — |
| Blended (3:1) | $0.525 (best) | $0.613 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Alibaba API |
| Limits | ||
| Context window | 131,072 tokens | 131,072 tokens |
| Max output | 8,192 tokens | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | qwen2-5-vl-7b-instruct | qwen2-5-14b-instruct |
| API providers | 1 | 1 |
| Released | Sep 2024 | Sep 2024 |
| Knowledge cutoff | Apr 2024 | 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.
- Qwen2.5-VL 7B Instruct$5.60
- Qwen2.5 14B Instruct$6.30
Which should you choose?
Which is better: Qwen2.5-VL 7B Instruct or Qwen2.5 14B Instruct?
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen2.5 14B Instruct (42). It leads on price and 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, Qwen2.5-VL 7B Instruct or Qwen2.5 14B Instruct?
Qwen2.5-VL 7B Instruct is cheaper at $0.35 input / $1.05 output per million tokens (official Alibaba API price). Qwen2.5 14B Instruct costs $0.35 input / $1.40 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for Qwen2.5-VL 7B Instruct versus $0.613 for Qwen2.5 14B Instruct (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Qwen2.5-VL 7B Instruct has not been scored yet and Qwen2.5 14B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5-VL 7B Instruct and Qwen2.5 14B 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?
Qwen2.5-VL 7B Instruct and Qwen2.5 14B Instruct share the same 131,072-token context window. Maximum output per response: Qwen2.5-VL 7B Instruct up to 8,192, Qwen2.5 14B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-VL 7B Instruct accepts text and images; Qwen2.5 14B Instruct accepts text. Qwen2.5-VL 7B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
Qwen2.5-VL 7B Instruct is the newest, released Sep 2024. Qwen2.5 14B Instruct came out Sep 2024. Knowledge cutoff: Qwen2.5-VL 7B Instruct Apr 2024, Qwen2.5 14B 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.
Related comparisons
- Add CodestralThree-way comparison
- Qwen2.5-VL 7B Instruct vs CodestralMistral AI
- Qwen2.5-VL 7B Instruct vs Aya Expanse 32BCohere
- Qwen2.5-VL 7B Instruct vs Llama-3.2-11B-Vision-InstructMeta
- Qwen2.5-VL 7B Instruct vs Llama 3.1 Nemotron 70B InstructNVIDIA
- Qwen2.5 14B Instruct vs Aya Expanse 32BCohere
- Qwen2.5 14B Instruct vs CodestralMistral AI
