Step 1 (32K) vs Qwen2.5-VL 72B Instruct vs Qwen-MT Plus
Qwen2.5-VL 72B Instruct comes out ahead, 30 to 18 and 12 on our weighted score, though Qwen-MT Plus is 12% cheaper per token.
StepFunStep 1 (32K)
18/100- ECI—
- Price$2.05 / $9.59
- Context33K
- Our pick
Alibaba (Qwen)Qwen2.5-VL 72B Instruct
30/100- ECI—
- Price$2.80 / $8.40
- Context131K
- Alibaba (Qwen)
Qwen-MT Plus
12/100- ECI—
- Price$2.46 / $7.37
- Context16K
Qwen2.5-VL 72B Instruct is our pick
Qwen2.5-VL 72B Instruct is the better all-round choice, scoring 30/100 against Step 1 (32K) (18) and Qwen-MT Plus (12). It leads on inputs & features and context window. 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 · Step 1 (32K) $3.94 · Qwen2.5-VL 72B Instruct $4.20 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-VL 72B InstructQwen2.5-VL 72B Instruct 131,072 · Step 1 (32K) 32,768 · Qwen-MT Plus 16,384 tokens
- Widest inputsQwen2.5-VL 72B InstructStep 1 (32K): Text · Qwen2.5-VL 72B Instruct: Text, Images · Qwen-MT Plus: Text
- Self-hostingQwen2.5-VL 72B InstructPublishes downloadable weights
| Measure | Weight | Step 1 (32K) | Qwen2.5-VL 72B Instruct | Qwen-MT Plus |
|---|---|---|---|---|
| Price | 50% | 22 | 20 | 23 |
| Inputs & features | 30% | 25 | 50 | 0 |
| Context window | 20% | 0 | 24 | 0 |
| Overall | 100% | 18/100 | 30/100 | 12/100 |
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 | Step 1 (32K)StepFun | Qwen2.5-VL 72B InstructAlibaba (Qwen) | Qwen-MT PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.05 (best) | $2.80 | $2.46 |
| Output | $9.59 | $8.40 | $7.37 (best) |
| Cached input | $0.41 | — | — |
| Blended (3:1) | $3.94 | $4.20 | $3.69 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official StepFun (Global) API | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 32,768 tokens | 131,072 tokens (best) | 16,384 tokens |
| Max output | 32,768 tokens (best) | 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 | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | step-1-32k | qwen2-5-vl-72b-instruct | qwen-mt-plus |
| API providers | 1 | 1 | 1 |
| Released | Jan 1, 2025 | Sep 2024 | Jan 2025 |
| Knowledge cutoff | Jun 2024 | 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.
- Step 1 (32K)$39.68
- Qwen2.5-VL 72B Instruct$44.80
- Qwen-MT Plus$39.34
Which should you choose?
Which is better: Step 1 (32K), Qwen2.5-VL 72B Instruct or Qwen-MT Plus?
Qwen2.5-VL 72B Instruct is the better all-round choice, scoring 30/100 against Step 1 (32K) (18) and Qwen-MT Plus (12). It leads on inputs & features and context window. 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, Step 1 (32K), Qwen2.5-VL 72B Instruct or Qwen-MT Plus?
Qwen-MT Plus is cheaper at $2.46 input / $7.37 output per million tokens (official Alibaba API price). Step 1 (32K) costs $2.05 input / $9.59 output per million tokens (official StepFun (Global) API price); Qwen2.5-VL 72B Instruct costs $2.80 input / $8.40 output per million tokens (official Alibaba 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 $3.94 for Step 1 (32K) (1.1× as much) and $4.20 for Qwen2.5-VL 72B Instruct (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Step 1 (32K) has not been scored yet, Qwen2.5-VL 72B Instruct has not been scored yet and Qwen-MT Plus has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Step 1 (32K), Qwen2.5-VL 72B Instruct and Qwen-MT Plus 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 32,768 for Step 1 (32K) and 16,384 for Qwen-MT Plus. Maximum output per response: Step 1 (32K) up to 32,768, Qwen2.5-VL 72B Instruct up to 8,192, Qwen-MT Plus up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Step 1 (32K) accepts text; Qwen2.5-VL 72B Instruct accepts text and images; Qwen-MT Plus 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; Step 1 (32K) and Qwen-MT Plus is proprietary.
Which is newer?
Step 1 (32K) is the newest, released Jan 1, 2025. Qwen-MT Plus came out Jan 2025; Qwen2.5-VL 72B Instruct came out Sep 2024. Knowledge cutoff: Step 1 (32K) Jun 2024, Qwen2.5-VL 72B Instruct Apr 2024, Qwen-MT Plus 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.
