ALLaM-2-7b vs Qwen3-VL Plus vs Qwen2.5-VL 72B Instruct
Qwen3-VL Plus comes out ahead, 57 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
Alibaba (Qwen)Qwen3-VL Plus
57/100- ECI—
- Price$0.20 / $1.60
- Context262K
- Alibaba (Qwen)
Qwen2.5-VL 72B Instruct
40/100- ECI—
- Price$0.905 / $1.00
- Context131K
Qwen3-VL Plus is our pick
Qwen3-VL Plus is the better all-round choice, scoring 57/100 against Qwen2.5-VL 72B Instruct (40) and ALLaM-2-7b (0). It leads on inputs & features and 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 priceQwen3-VL PlusQwen3-VL Plus $0.55 · Qwen2.5-VL 72B Instruct $0.93 per 1M tokens (3:1 blend) · ALLaM-2-7b unpriced
- Longest contextQwen3-VL PlusQwen3-VL Plus 262,144 · Qwen2.5-VL 72B Instruct 131,072 · ALLaM-2-7b 4,096 tokens
- Widest inputsQwen3-VL Plus and Qwen2.5-VL 72B InstructALLaM-2-7b: Text · Qwen3-VL Plus: Text, Images · Qwen2.5-VL 72B Instruct: Text, Images
- Self-hostingALLaM-2-7b and Qwen2.5-VL 72B InstructPublishes downloadable weights
| Measure | Weight | ALLaM-2-7b | Qwen3-VL Plus | Qwen2.5-VL 72B Instruct |
|---|---|---|---|---|
| Inputs & features | 60% | 0 | 70 | 50 |
| Context window | 40% | 0 | 37 | 25 |
| Overall | 100% | 0/100 | 57/100 | 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 | Qwen3-VL PlusAlibaba (Qwen) | Qwen2.5-VL 72B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $0.20 (best) | $0.905 |
| Output | — | $1.60 | $1.00 (best) |
| Cached input | — | $0.04 | — |
| Blended (3:1) | — | $0.55 (best) | $0.93 |
| Long-context rate | — | Over 32K: $0.30 / $2.40 | Same rate |
| Price source | — | Official Alibaba API | Median of 6 providers |
| Limits | |||
| Context window | 4,096 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 4,096 tokens | 32,768 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 | No | No |
| Reasoning | No | Yes | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | qwen3-vl-plus | — |
| API providers | 2 | 6 (best) | 6 (best) |
| Released | Jan 23, 2025 | Sep 23, 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—
- Qwen3-VL Plus$5.20
- Qwen2.5-VL 72B Instruct$11.06
Which should you choose?
Which is better: ALLaM-2-7b, Qwen3-VL Plus or Qwen2.5-VL 72B Instruct?
Qwen3-VL Plus is the better all-round choice, scoring 57/100 against Qwen2.5-VL 72B Instruct (40) and ALLaM-2-7b (0). It leads on inputs & features and 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, Qwen3-VL Plus or Qwen2.5-VL 72B Instruct?
Qwen3-VL Plus is cheaper at $0.20 input / $1.60 output per million tokens (official Alibaba 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 $0.55 per million tokens for Qwen3-VL Plus versus $0.93 for Qwen2.5-VL 72B Instruct (1.7× 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, Qwen3-VL Plus 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, Qwen3-VL Plus 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?
Qwen3-VL Plus has the largest context window at 262,144 tokens, against 131,072 for Qwen2.5-VL 72B Instruct and 4,096 for ALLaM-2-7b. Maximum output per response: ALLaM-2-7b up to 4,096, Qwen3-VL Plus up to 32,768, Qwen2.5-VL 72B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
ALLaM-2-7b accepts text; Qwen3-VL Plus accepts text and images; Qwen2.5-VL 72B Instruct accepts text and images. Qwen3-VL Plus handles the widest range of inputs.
Are any of these open source?
ALLaM-2-7b and Qwen2.5-VL 72B Instruct publishes its weights and can be self-hosted; Qwen3-VL Plus is proprietary.
Which is newer?
Qwen3-VL Plus is the newest, released Sep 23, 2025. Qwen2.5-VL 72B Instruct came out Jan 26, 2025; ALLaM-2-7b came out Jan 23, 2025. Knowledge cutoff: Qwen3-VL Plus 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.
