Step 1 (32K) vs Pixtral Large (25.02) vs Qwen-MT Plus
Pixtral Large (25.02) comes out ahead, 33 to 18 and 12 on our weighted score, and it is the cheaper option too.
StepFunStep 1 (32K)
18/100- ECI—
- Price$2.05 / $9.59
- Context33K
- Our pick
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
Alibaba (Qwen)Qwen-MT Plus
12/100- ECI—
- Price$2.46 / $7.37
- Context16K
Pixtral Large (25.02) is our pick
Pixtral Large (25.02) is the better all-round choice, scoring 33/100 against Step 1 (32K) (18) and Qwen-MT Plus (12). It leads on price, 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 pricePixtral Large (25.02)Pixtral Large (25.02) $3.00 · Qwen-MT Plus $3.69 · Step 1 (32K) $3.94 per 1M tokens (3:1 blend)
- Longest contextPixtral Large (25.02)Pixtral Large (25.02) 128,000 · Step 1 (32K) 32,768 · Qwen-MT Plus 16,384 tokens
- Widest inputsPixtral Large (25.02)Step 1 (32K): Text · Pixtral Large (25.02): Text, Images · Qwen-MT Plus: Text
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Step 1 (32K) | Pixtral Large (25.02) | Qwen-MT Plus |
|---|---|---|---|---|
| Price | 50% | 22 | 27 | 23 |
| Inputs & features | 30% | 25 | 50 | 0 |
| Context window | 20% | 0 | 24 | 0 |
| Overall | 100% | 18/100 | 33/100 | 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 | Pixtral Large (25.02)Mistral AI | Qwen-MT PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.05 | $2.00 (best) | $2.46 |
| Output | $9.59 | $6.00 (best) | $7.37 |
| Cached input | $0.41 | — | — |
| Blended (3:1) | $3.94 | $3.00 (best) | $3.69 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official StepFun (Global) API | Median of 3 providers | Official Alibaba API |
| Limits | |||
| Context window | 32,768 tokens | 128,000 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 | Proprietary | Proprietary |
| API model ID | step-1-32k | — | qwen-mt-plus |
| API providers | 1 | 3 (best) | 1 |
| Released | Jan 1, 2025 | Apr 8, 2025 | Jan 2025 |
| Knowledge cutoff | Jun 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
- Pixtral Large (25.02)$32.00
- Qwen-MT Plus$39.34
Which should you choose?
Which is better: Step 1 (32K), Pixtral Large (25.02) or Qwen-MT Plus?
Pixtral Large (25.02) is the better all-round choice, scoring 33/100 against Step 1 (32K) (18) and Qwen-MT Plus (12). It leads on price, 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), Pixtral Large (25.02) or Qwen-MT Plus?
Pixtral Large (25.02) is cheaper at $2.00 input / $6.00 output per million tokens (median across 3 API providers). Qwen-MT Plus costs $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). At a typical mix of three input tokens to one output token, that is $3.00 per million tokens for Pixtral Large (25.02) versus $3.69 for Qwen-MT Plus (1.2× as much) and $3.94 for Step 1 (32K) (1.3× 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, Pixtral Large (25.02) 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), Pixtral Large (25.02) 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?
Pixtral Large (25.02) has the largest context window at 128,000 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, Pixtral Large (25.02) 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; Pixtral Large (25.02) accepts text and images; Qwen-MT Plus accepts text. Pixtral Large (25.02) handles the widest range of inputs.
Are any of these open source?
No. Step 1 (32K), Pixtral Large (25.02) and Qwen-MT Plus are proprietary and only available through APIs and apps.
Which is newer?
Pixtral Large (25.02) is the newest, released Apr 8, 2025. Step 1 (32K) came out Jan 1, 2025; Qwen-MT Plus came out Jan 2025. Knowledge cutoff: Step 1 (32K) Jun 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.
