Qwen-MT Plus vs Qwen2.5-VL 72B Instruct vs Step 2 (16K)
Qwen2.5-VL 72B Instruct comes out ahead, 30 to 12 and 11 on our weighted score, though Qwen-MT Plus is 12% cheaper per token.
Alibaba (Qwen)Qwen-MT Plus
12/100- ECI—
- Price$2.46 / $7.37
- Context16K
- Our pickAlibaba (Qwen)
Qwen2.5-VL 72B Instruct
30/100- ECI—
- Price$2.80 / $8.40
- Context131K
StepFunStep 2 (16K)
11/100- ECI—
- Price$5.21 / $16.44
- Context16K
Qwen2.5-VL 72B Instruct is our pick
Qwen2.5-VL 72B Instruct is the better all-round choice, scoring 30/100 against Qwen-MT Plus (12) and Step 2 (16K) (11). It leads on inputs & features and context window. Qwen-MT Plus wins on price. 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 priceQwen-MT PlusQwen-MT Plus $3.69 · Qwen2.5-VL 72B Instruct $4.20 · Step 2 (16K) $8.02 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-VL 72B InstructQwen2.5-VL 72B Instruct 131,072 · Qwen-MT Plus 16,384 · Step 2 (16K) 16,384 tokens
- Widest inputsQwen2.5-VL 72B InstructQwen-MT Plus: Text · Qwen2.5-VL 72B Instruct: Text, Images · Step 2 (16K): Text
- Self-hostingQwen2.5-VL 72B InstructPublishes downloadable weights
| Measure | Weight | Qwen-MT Plus | Qwen2.5-VL 72B Instruct | Step 2 (16K) |
|---|---|---|---|---|
| Price | 50% | 23 | 20 | 7 |
| Inputs & features | 30% | 0 | 50 | 25 |
| Context window | 20% | 0 | 24 | 0 |
| Overall | 100% | 12/100 | 30/100 | 11/100 |
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 | Qwen-MT PlusAlibaba (Qwen) | Qwen2.5-VL 72B InstructAlibaba (Qwen) | Step 2 (16K)StepFun |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.46 (best) | $2.80 | $5.21 |
| Output | $7.37 (best) | $8.40 | $16.44 |
| Cached input | — | — | $1.04 |
| Blended (3:1) | $3.69 (best) | $4.20 | $8.02 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Alibaba API | Official StepFun (Global) API |
| Limits | |||
| Context window | 16,384 tokens | 131,072 tokens (best) | 16,384 tokens |
| Max output | 8,192 tokens | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | qwen-mt-plus | qwen2-5-vl-72b-instruct | step-2-16k |
| API providers | 1 | 1 | 1 |
| Released | Jan 2025 | Sep 2024 | Jan 1, 2025 |
| Knowledge cutoff | Apr 2024 | Apr 2024 | Jun 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen-MT Plus$39.34
- Qwen2.5-VL 72B Instruct$44.80
- Step 2 (16K)$84.98
Which should you choose?
Which is better: Qwen-MT Plus, Qwen2.5-VL 72B Instruct or Step 2 (16K)?
Qwen2.5-VL 72B Instruct is the better all-round choice, scoring 30/100 against Qwen-MT Plus (12) and Step 2 (16K) (11). It leads on inputs & features and context window. Qwen-MT Plus wins on price. 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, Qwen-MT Plus, Qwen2.5-VL 72B Instruct or Step 2 (16K)?
Qwen-MT Plus is cheaper at $2.46 input / $7.37 output per million tokens (official Alibaba API price). Qwen2.5-VL 72B Instruct costs $2.80 input / $8.40 output per million tokens (official Alibaba API price); Step 2 (16K) costs $5.21 input / $16.44 output per million tokens (official StepFun (Global) API price). At a typical mix of three input tokens to one output token, that is $3.69 per million tokens for Qwen-MT Plus versus $4.20 for Qwen2.5-VL 72B Instruct (1.1× as much) and $8.02 for Step 2 (16K) (2.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen-MT Plus has not been scored yet, Qwen2.5-VL 72B Instruct has not been scored yet and Step 2 (16K) has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen-MT Plus, Qwen2.5-VL 72B Instruct and Step 2 (16K) yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen-MT Plus 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 16,384 for Qwen-MT Plus and 16,384 for Step 2 (16K). Maximum output per response: Qwen-MT Plus up to 8,192, Qwen2.5-VL 72B Instruct up to 8,192, Step 2 (16K) up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Qwen-MT Plus accepts text; Qwen2.5-VL 72B Instruct accepts text and images; Step 2 (16K) accepts text. Qwen2.5-VL 72B Instruct handles the widest range of inputs.
Are any of these open source?
Qwen2.5-VL 72B Instruct publishes its weights and can be self-hosted; Qwen-MT Plus and Step 2 (16K) is proprietary.
Which is newer?
Qwen-MT Plus is the newest, released Jan 2025. Step 2 (16K) came out Jan 1, 2025; Qwen2.5-VL 72B Instruct came out Sep 2024. Knowledge cutoff: Qwen-MT Plus Apr 2024, Qwen2.5-VL 72B Instruct Apr 2024, Step 2 (16K) Jun 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.
