Hy-MT2-1.8B vs Ling 3.0 Flash vs DiffusionGemma 26B A4B IT
DiffusionGemma 26B A4B IT comes out ahead, 75 to 65 and 49 on our weighted score, and it is the cheaper option too.
TencentHy-MT2-1.8B
49/100- ECI—
- Price$0.044 / $0.177
- Context8K
inclusionAILing 3.0 Flash
65/100- ECI—
- Price$0.06 / $0.18
- Context262K
- Our pick
GoogleDiffusionGemma 26B A4B IT
75/100- ECI—
- Price$0.05 / $0.15
- Context262K
DiffusionGemma 26B A4B IT is our pick
DiffusionGemma 26B A4B IT is the better all-round choice, scoring 75/100 against Ling 3.0 Flash (65) and Hy-MT2-1.8B (49). It leads on inputs & features. 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 $0.09 per 1M tokens (3:1 blend)
- Longest contextLing 3.0 Flash and DiffusionGemma 26B A4B ITLing 3.0 Flash 262,144 · DiffusionGemma 26B A4B IT 262,144 · Hy-MT2-1.8B 8,192 tokens
- Widest inputsDiffusionGemma 26B A4B ITHy-MT2-1.8B: Text · Ling 3.0 Flash: Text · DiffusionGemma 26B A4B IT: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Hy-MT2-1.8B | Ling 3.0 Flash | DiffusionGemma 26B A4B IT |
|---|---|---|---|---|
| Price | 50% | 98 | 95 | 99 |
| Inputs & features | 30% | 0 | 35 | 60 |
| Context window | 20% | 0 | 37 | 37 |
| Overall | 100% | 49/100 | 65/100 | 75/100 |
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 | Hy-MT2-1.8BTencent | Ling 3.0 FlashinclusionAI | DiffusionGemma 26B A4B ITGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.044 (best) | $0.06 | $0.05 |
| Output | $0.177 | $0.18 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.077 | $0.09 | $0.075 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 7 providers | Median of 1 providers |
| Limits | |||
| Context window | 8,192 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 4,096 tokens | 32,768 tokens (best) | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenMIT | OpenApache 2.0 |
| API model ID | — | — | — |
| API providers | 3 | 9 (best) | 2 |
| Released | May 21, 2026 | Jul 23, 2026 | Jun 9, 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.
- Hy-MT2-1.8B$0.794
- Ling 3.0 Flash$0.96
- DiffusionGemma 26B A4B IT$0.80
Which should you choose?
Which is better: Hy-MT2-1.8B, Ling 3.0 Flash or DiffusionGemma 26B A4B IT?
DiffusionGemma 26B A4B IT is the better all-round choice, scoring 75/100 against Ling 3.0 Flash (65) and Hy-MT2-1.8B (49). It leads on inputs & features. 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, Hy-MT2-1.8B, Ling 3.0 Flash or DiffusionGemma 26B A4B IT?
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 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 (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Hy-MT2-1.8B has not been scored yet, Ling 3.0 Flash has not been scored yet and DiffusionGemma 26B A4B IT has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Hy-MT2-1.8B, Ling 3.0 Flash and DiffusionGemma 26B A4B IT 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?
Ling 3.0 Flash and DiffusionGemma 26B A4B IT have the largest context windows (262,144 and 262,144 tokens), against 8,192 for Hy-MT2-1.8B. Maximum output per response: Hy-MT2-1.8B up to 4,096, Ling 3.0 Flash up to 32,768, DiffusionGemma 26B A4B IT up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Hy-MT2-1.8B accepts text; Ling 3.0 Flash accepts text; DiffusionGemma 26B A4B IT accepts text and images. DiffusionGemma 26B A4B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (MIT and Apache 2.0), so you can self-host them.
Which is newer?
Ling 3.0 Flash is the newest, released Jul 23, 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.
