DiffusionGemma 26B A4B IT vs Schematron V2 Turbo vs Hy-MT2-1.8B
DiffusionGemma 26B A4B IT comes out ahead, 75 to 55 and 49 on our weighted score, though Schematron V2 Turbo is 20% cheaper per token.
- Our pick
GoogleDiffusionGemma 26B A4B IT
75/100- ECI—
- Price$0.05 / $0.15
- Context262K
Inference.net
Schematron V2 Turbo
55/100- ECI—
- Price$0.03 / $0.15
- Context128K
TencentHy-MT2-1.8B
49/100- ECI—
- Price$0.044 / $0.177
- Context8K
DiffusionGemma 26B A4B IT is our pick
DiffusionGemma 26B A4B IT is the better all-round choice, scoring 75/100 against Schematron V2 Turbo (55) 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 priceSchematron V2 TurboSchematron V2 Turbo $0.06 · DiffusionGemma 26B A4B IT $0.075 · Hy-MT2-1.8B $0.077 per 1M tokens (3:1 blend)
- Longest contextDiffusionGemma 26B A4B ITDiffusionGemma 26B A4B IT 262,144 · Schematron V2 Turbo 128,000 · Hy-MT2-1.8B 8,192 tokens
- Widest inputsDiffusionGemma 26B A4B ITDiffusionGemma 26B A4B IT: Text, Images · Schematron V2 Turbo: Text · Hy-MT2-1.8B: Text
- Self-hostingDiffusionGemma 26B A4B IT and Hy-MT2-1.8BPublishes downloadable weights (Apache 2.0)
| Measure | Weight | DiffusionGemma 26B A4B IT | Schematron V2 Turbo | Hy-MT2-1.8B |
|---|---|---|---|---|
| Price | 50% | 99 | 100 | 98 |
| Inputs & features | 30% | 60 | 0 | 0 |
| Context window | 20% | 37 | 24 | 0 |
| Overall | 100% | 75/100 | 55/100 | 49/100 |
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 | Schematron V2 TurboInference.net | Hy-MT2-1.8BTencent |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.05 | $0.03 (best) | $0.044 |
| Output | $0.15 (best) | $0.15 (best) | $0.177 |
| Cached input | — | — | — |
| Blended (3:1) | $0.075 | $0.06 (best) | $0.077 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 5 providers | Median of 3 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 128,000 tokens | 8,192 tokens |
| Max output | 32,768 tokens (best) | 8,192 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 | Proprietary | Open |
| API model ID | — | — | — |
| API providers | 2 | 5 (best) | 3 |
| Released | Jun 9, 2026 | Apr 16, 2026 | May 21, 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
- Schematron V2 Turbo$0.60
- Hy-MT2-1.8B$0.794
Which should you choose?
Which is better: DiffusionGemma 26B A4B IT, Schematron V2 Turbo or Hy-MT2-1.8B?
DiffusionGemma 26B A4B IT is the better all-round choice, scoring 75/100 against Schematron V2 Turbo (55) 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, Schematron V2 Turbo or Hy-MT2-1.8B?
Schematron V2 Turbo is cheaper at $0.03 input / $0.15 output per million tokens (median across 5 API providers). DiffusionGemma 26B A4B IT costs $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). At a typical mix of three input tokens to one output token, that is $0.06 per million tokens for Schematron V2 Turbo versus $0.075 for DiffusionGemma 26B A4B IT (1.3× as much) and $0.077 for Hy-MT2-1.8B (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, Schematron V2 Turbo has not been scored yet and Hy-MT2-1.8B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for DiffusionGemma 26B A4B IT, Schematron V2 Turbo and Hy-MT2-1.8B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Schematron V2 Turbo and Hy-MT2-1.8B 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 Turbo and 8,192 for Hy-MT2-1.8B. Maximum output per response: DiffusionGemma 26B A4B IT up to 32,768, Schematron V2 Turbo up to 8,192, Hy-MT2-1.8B up to 4,096 tokens.
Which can read images, PDFs, audio or video?
DiffusionGemma 26B A4B IT accepts text and images; Schematron V2 Turbo accepts text; Hy-MT2-1.8B 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 Turbo 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 Turbo 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.
