Baichuan M2 32B vs Granite-4.0-H-Small vs Qwen3 VL Flash (Alibaba Cloud)
Qwen3 VL Flash (Alibaba Cloud) comes out ahead, 72 to 61 and 58 on our weighted score.
BaichuanBaichuan M2 32B
58/100- ECI—
- Price$0.07 / $0.07
- Context131K
IBMGranite-4.0-H-Small
61/100- ECI—
- Price$0.064 / $0.265
- Context131K
- Our pick
Alibaba (Qwen)Qwen3 VL Flash (Alibaba Cloud)
72/100- ECI—
- Price$0.022 / $0.215
- Context262K
Qwen3 VL Flash (Alibaba Cloud) is our pick
Qwen3 VL Flash (Alibaba Cloud) is the better all-round choice, scoring 72/100 against Granite-4.0-H-Small (61) and Baichuan M2 32B (58). 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 priceBaichuan M2 32BBaichuan M2 32B $0.07 · Qwen3 VL Flash (Alibaba Cloud) $0.07 · Granite-4.0-H-Small $0.114 per 1M tokens (3:1 blend)
- Longest contextQwen3 VL Flash (Alibaba Cloud)Qwen3 VL Flash (Alibaba Cloud) 262,144 · Baichuan M2 32B 131,072 · Granite-4.0-H-Small 131,072 tokens
- Widest inputsQwen3 VL Flash (Alibaba Cloud)Baichuan M2 32B: Text · Granite-4.0-H-Small: Text · Qwen3 VL Flash (Alibaba Cloud): Text, Images
- Self-hostingBaichuan M2 32B and Granite-4.0-H-SmallPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Baichuan M2 32B | Granite-4.0-H-Small | Qwen3 VL Flash (Alibaba Cloud) |
|---|---|---|---|---|
| Price | 50% | 100 | 91 | 100 |
| Inputs & features | 30% | 10 | 35 | 50 |
| Context window | 20% | 25 | 25 | 37 |
| Overall | 100% | 58/100 | 61/100 | 72/100 |
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 | Baichuan M2 32BBaichuan | Granite-4.0-H-SmallIBM | Qwen3 VL Flash (Alibaba Cloud)Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.07 | $0.064 | $0.022 (best) |
| Output | $0.07 (best) | $0.265 | $0.215 |
| Cached input | — | — | — |
| Blended (3:1) | $0.07 (best) | $0.114 | $0.07 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official watsonx.ai API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 131,072 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenApache-2.0 | Open | Proprietary |
| API model ID | — | ibm/granite-4-h-small | — |
| API providers | 1 | 1 | 1 |
| Released | Aug 13, 2025 | Oct 2, 2025 | Oct 15, 2025 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Baichuan M2 32B$0.84
- Granite-4.0-H-Small$1.17
- Qwen3 VL Flash (Alibaba Cloud)$0.65
Which should you choose?
Which is better: Baichuan M2 32B, Granite-4.0-H-Small or Qwen3 VL Flash (Alibaba Cloud)?
Qwen3 VL Flash (Alibaba Cloud) is the better all-round choice, scoring 72/100 against Granite-4.0-H-Small (61) and Baichuan M2 32B (58). 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, Baichuan M2 32B, Granite-4.0-H-Small or Qwen3 VL Flash (Alibaba Cloud)?
Baichuan M2 32B is cheaper at $0.07 input / $0.07 output per million tokens (median across 1 API provider). Qwen3 VL Flash (Alibaba Cloud) costs $0.022 input / $0.215 output per million tokens (median across 1 API provider); Granite-4.0-H-Small costs $0.064 input / $0.265 output per million tokens (official watsonx.ai API price). At a typical mix of three input tokens to one output token, that is $0.07 per million tokens for Baichuan M2 32B versus $0.07 for Qwen3 VL Flash (Alibaba Cloud) (1× as much) and $0.114 for Granite-4.0-H-Small (1.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Baichuan M2 32B has not been scored yet, Granite-4.0-H-Small has not been scored yet and Qwen3 VL Flash (Alibaba Cloud) has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Baichuan M2 32B, Granite-4.0-H-Small and Qwen3 VL Flash (Alibaba Cloud) yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Baichuan M2 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen3 VL Flash (Alibaba Cloud) has the largest context window at 262,144 tokens, against 131,072 for Baichuan M2 32B and 131,072 for Granite-4.0-H-Small. Maximum output per response: Baichuan M2 32B up to 131,072, Granite-4.0-H-Small up to 131,072, Qwen3 VL Flash (Alibaba Cloud) up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Baichuan M2 32B accepts text; Granite-4.0-H-Small accepts text; Qwen3 VL Flash (Alibaba Cloud) accepts text and images. Qwen3 VL Flash (Alibaba Cloud) handles the widest range of inputs.
Are any of these open source?
Baichuan M2 32B and Granite-4.0-H-Small publishes its weights (Apache-2.0) and can be self-hosted; Qwen3 VL Flash (Alibaba Cloud) is proprietary.
Which is newer?
Qwen3 VL Flash (Alibaba Cloud) is the newest, released Oct 15, 2025. Granite-4.0-H-Small came out Oct 2, 2025; Baichuan M2 32B came out Aug 13, 2025.
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.
