DiffusionGemma 26B A4B IT vs Hy-MT2-1.8B vs Schematron V2 Small
DiffusionGemma 26B A4B IT comes out ahead, 75 to 52 and 49 on our weighted score, and it is the cheaper option too.
- Our pick
GoogleDiffusionGemma 26B A4B IT
75/100- ECI—
- Price$0.05 / $0.15
- Context262K
TencentHy-MT2-1.8B
49/100- ECI—
- Price$0.044 / $0.177
- Context8K
Inference.net
Schematron V2 Small
52/100- ECI—
- Price$0.05 / $0.23
- Context128K
DiffusionGemma 26B A4B IT is our pick
DiffusionGemma 26B A4B IT is the better all-round choice, scoring 75/100 against Schematron V2 Small (52) and Hy-MT2-1.8B (49). 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 priceDiffusionGemma 26B A4B ITDiffusionGemma 26B A4B IT $0.075 · Hy-MT2-1.8B $0.077 · Schematron V2 Small $0.095 per 1M tokens (3:1 blend)
- Longest contextDiffusionGemma 26B A4B ITDiffusionGemma 26B A4B IT 262,144 · Schematron V2 Small 128,000 · Hy-MT2-1.8B 8,192 tokens
- Widest inputsDiffusionGemma 26B A4B ITDiffusionGemma 26B A4B IT: Text, Images · Hy-MT2-1.8B: Text · Schematron V2 Small: Text
- Self-hostingDiffusionGemma 26B A4B IT and Hy-MT2-1.8BPublishes downloadable weights (Apache 2.0)
| Measure | Weight | DiffusionGemma 26B A4B IT | Hy-MT2-1.8B | Schematron V2 Small |
|---|---|---|---|---|
| Price | 50% | 99 | 98 | 94 |
| Inputs & features | 30% | 60 | 0 | 0 |
| Context window | 20% | 37 | 0 | 24 |
| Overall | 100% | 75/100 | 49/100 | 52/100 |
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 | DiffusionGemma 26B A4B ITGoogle | Hy-MT2-1.8BTencent | Schematron V2 SmallInference.net |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.05 | $0.044 (best) | $0.05 |
| Output | $0.15 (best) | $0.177 | $0.23 |
| Cached input | — | — | — |
| Blended (3:1) | $0.075 (best) | $0.077 | $0.095 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 3 providers | Median of 5 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 8,192 tokens | 128,000 tokens |
| Max output | 32,768 tokens (best) | 4,096 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | No | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache 2.0 | Open | Proprietary |
| API model ID | — | — | — |
| API providers | 2 | 3 | 5 (best) |
| Released | Jun 9, 2026 | May 21, 2026 | Apr 16, 2026 |
| Knowledge cutoff | Jan 2025 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DiffusionGemma 26B A4B IT$0.80
- Hy-MT2-1.8B$0.794
- Schematron V2 Small$0.96
Which should you choose?
Which is better: DiffusionGemma 26B A4B IT, Hy-MT2-1.8B or Schematron V2 Small?
DiffusionGemma 26B A4B IT is the better all-round choice, scoring 75/100 against Schematron V2 Small (52) and Hy-MT2-1.8B (49). 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, DiffusionGemma 26B A4B IT, Hy-MT2-1.8B or Schematron V2 Small?
DiffusionGemma 26B A4B IT is cheaper at $0.05 input / $0.15 output per million tokens (median across 1 API provider). Hy-MT2-1.8B costs $0.044 input / $0.177 output per million tokens (median across 3 API providers); Schematron V2 Small costs $0.05 input / $0.23 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $0.075 per million tokens for DiffusionGemma 26B A4B IT versus $0.077 for Hy-MT2-1.8B (1× as much) and $0.095 for Schematron V2 Small (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DiffusionGemma 26B A4B IT has not been scored yet, Hy-MT2-1.8B has not been scored yet and Schematron V2 Small has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for DiffusionGemma 26B A4B IT, Hy-MT2-1.8B and Schematron V2 Small yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Hy-MT2-1.8B and Schematron V2 Small does not support tool calling, which most coding agents need.
Which has the bigger context window?
DiffusionGemma 26B A4B IT has the largest context window at 262,144 tokens, against 128,000 for Schematron V2 Small and 8,192 for Hy-MT2-1.8B. Maximum output per response: DiffusionGemma 26B A4B IT up to 32,768, Hy-MT2-1.8B up to 4,096, Schematron V2 Small up to 4,096 tokens.
Which can read images, PDFs, audio or video?
DiffusionGemma 26B A4B IT accepts text and images; Hy-MT2-1.8B accepts text; Schematron V2 Small accepts text. DiffusionGemma 26B A4B IT handles the widest range of inputs.
Are any of these open source?
DiffusionGemma 26B A4B IT and Hy-MT2-1.8B publishes its weights (Apache 2.0) and can be self-hosted; Schematron V2 Small is proprietary.
Which is newer?
DiffusionGemma 26B A4B IT is the newest, released Jun 9, 2026. Hy-MT2-1.8B came out May 21, 2026; Schematron V2 Small came out Apr 16, 2026. Knowledge cutoff: DiffusionGemma 26B A4B IT Jan 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.
