DiffusionGemma 26B A4B IT vs Hy-MT2-1.8B vs Ling 3.0 Flash VL
Too close to call on our weighted score (Ling 3.0 Flash VL 76, DiffusionGemma 26B A4B IT 75, Hy-MT2-1.8B 49). The right pick depends on what you value most.
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
inclusionAILing 3.0 Flash VL
76/100- ECI—
- Price$0.06 / $0.18
- Context262K
Too close to call
It is close. Our weighted score puts them within 1 points (Ling 3.0 Flash VL 76/100, DiffusionGemma 26B A4B IT 75/100, Hy-MT2-1.8B 49/100), so choose by what matters most for your work: DiffusionGemma 26B A4B IT on price. 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 · Ling 3.0 Flash VL $0.09 per 1M tokens (3:1 blend)
- Longest contextDiffusionGemma 26B A4B IT and Ling 3.0 Flash VLDiffusionGemma 26B A4B IT 262,144 · Ling 3.0 Flash VL 262,144 · Hy-MT2-1.8B 8,192 tokens
- Widest inputsLing 3.0 Flash VLDiffusionGemma 26B A4B IT: Text, Images · Hy-MT2-1.8B: Text · Ling 3.0 Flash VL: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DiffusionGemma 26B A4B IT | Hy-MT2-1.8B | Ling 3.0 Flash VL |
|---|---|---|---|---|
| Price | 50% | 99 | 98 | 95 |
| Inputs & features | 30% | 60 | 0 | 70 |
| Context window | 20% | 37 | 0 | 37 |
| Overall | 100% | 75/100 | 49/100 | 76/100 |
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 | Ling 3.0 Flash VLinclusionAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.05 | $0.044 (best) | $0.06 |
| Output | $0.15 (best) | $0.177 | $0.18 |
| Cached input | — | — | — |
| Blended (3:1) | $0.075 (best) | $0.077 | $0.09 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 3 providers | Median of 7 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 8,192 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens (best) | 4,096 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache 2.0 | Open | OpenMIT |
| API model ID | — | — | — |
| API providers | 2 | 3 | 7 (best) |
| Released | Jun 9, 2026 | May 21, 2026 | Sep 10, 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
- Ling 3.0 Flash VL$0.96
Which should you choose?
Which is better: DiffusionGemma 26B A4B IT, Hy-MT2-1.8B or Ling 3.0 Flash VL?
It is close. Our weighted score puts them within 1 points (Ling 3.0 Flash VL 76/100, DiffusionGemma 26B A4B IT 75/100, Hy-MT2-1.8B 49/100), so choose by what matters most for your work: DiffusionGemma 26B A4B IT on price. 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 Ling 3.0 Flash VL?
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); Ling 3.0 Flash VL costs $0.06 input / $0.18 output per million tokens (median across 7 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 $0.09 for Ling 3.0 Flash VL (1.2× 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 Ling 3.0 Flash VL 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 Ling 3.0 Flash VL 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 does not support tool calling, which most coding agents need.
Which has the bigger context window?
DiffusionGemma 26B A4B IT and Ling 3.0 Flash VL have the largest context windows (262,144 and 262,144 tokens), against 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, Ling 3.0 Flash VL up to 32,768 tokens.
Which can read images, PDFs, audio or video?
DiffusionGemma 26B A4B IT accepts text and images; Hy-MT2-1.8B accepts text; Ling 3.0 Flash VL accepts text, images and video. Ling 3.0 Flash VL handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0 and MIT), so you can self-host them.
Which is newer?
Ling 3.0 Flash VL is the newest, released Sep 10, 2026. DiffusionGemma 26B A4B IT came out Jun 9, 2026; Hy-MT2-1.8B came out May 21, 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.
