Gemma 4 E2B IT vs DiffusionGemma 26B A4B IT vs Qwen3.5 4B
Too close to call on our weighted score (Qwen3.5 4B 78, Gemma 4 E2B IT 76, DiffusionGemma 26B A4B IT 75). The right pick depends on what you value most.
GoogleGemma 4 E2B IT
76/100- ECI—
- Price$0.04 / $0.08
- Context131K
- Google
DiffusionGemma 26B A4B IT
75/100- ECI—
- Price$0.05 / $0.15
- Context262K
Alibaba (Qwen)Qwen3.5 4B
78/100- ECI—
- Price$0.04 / $0.07
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3.5 4B 78/100, Gemma 4 E2B IT 76/100, DiffusionGemma 26B A4B IT 75/100), so choose by what matters most for your work: Qwen3.5 4B 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 priceQwen3.5 4BQwen3.5 4B $0.048 · Gemma 4 E2B IT $0.05 · DiffusionGemma 26B A4B IT $0.075 per 1M tokens (3:1 blend)
- Longest contextDiffusionGemma 26B A4B IT and Qwen3.5 4BDiffusionGemma 26B A4B IT 262,144 · Qwen3.5 4B 262,144 · Gemma 4 E2B IT 131,072 tokens
- Widest inputsGemma 4 E2B IT and Qwen3.5 4BGemma 4 E2B IT: Text, Images, Audio · DiffusionGemma 26B A4B IT: Text, Images · Qwen3.5 4B: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Gemma 4 E2B IT | DiffusionGemma 26B A4B IT | Qwen3.5 4B |
|---|---|---|---|---|
| Price | 50% | 100 | 99 | 100 |
| Inputs & features | 30% | 70 | 60 | 70 |
| Context window | 20% | 25 | 37 | 37 |
| Overall | 100% | 76/100 | 75/100 | 78/100 |
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 | Gemma 4 E2B ITGoogle | DiffusionGemma 26B A4B ITGoogle | Qwen3.5 4BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 55.8% |
| Price per million tokens | |||
| Input | $0.04 (best) | $0.05 | $0.04 (best) |
| Output | $0.08 | $0.15 | $0.07 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.05 | $0.075 | $0.048 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 1 providers | Median of 3 providers |
| Limits | |||
| Context window | 131,072 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 8,192 tokens | 32,768 tokens | 81,920 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | Yes | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | OpenApache-2.0 |
| API model ID | — | — | — |
| API providers | 1 | 2 | 5 (best) |
| Released | Apr 2, 2026 | Jun 9, 2026 | Mar 2, 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.
- Gemma 4 E2B IT$0.56
- DiffusionGemma 26B A4B IT$0.80
- Qwen3.5 4B$0.54
Which should you choose?
Which is better: Gemma 4 E2B IT, DiffusionGemma 26B A4B IT or Qwen3.5 4B?
It is close. Our weighted score puts them within 2 points (Qwen3.5 4B 78/100, Gemma 4 E2B IT 76/100, DiffusionGemma 26B A4B IT 75/100), so choose by what matters most for your work: Qwen3.5 4B 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, Gemma 4 E2B IT, DiffusionGemma 26B A4B IT or Qwen3.5 4B?
Qwen3.5 4B is cheaper at $0.04 input / $0.07 output per million tokens (median across 3 API providers). Gemma 4 E2B IT costs $0.04 input / $0.08 output per million tokens (median across 1 API provider); DiffusionGemma 26B A4B IT costs $0.05 input / $0.15 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.048 per million tokens for Qwen3.5 4B versus $0.05 for Gemma 4 E2B IT (1.1× as much) and $0.075 for DiffusionGemma 26B A4B IT (1.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemma 4 E2B IT has not been scored yet, DiffusionGemma 26B A4B IT has not been scored yet and Qwen3.5 4B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 4 E2B IT, DiffusionGemma 26B A4B IT and Qwen3.5 4B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
DiffusionGemma 26B A4B IT and Qwen3.5 4B have the largest context windows (262,144 and 262,144 tokens), against 131,072 for Gemma 4 E2B IT. Maximum output per response: Gemma 4 E2B IT up to 8,192, DiffusionGemma 26B A4B IT up to 32,768, Qwen3.5 4B up to 81,920 tokens.
Which can read images, PDFs, audio or video?
Gemma 4 E2B IT accepts text, images and audio; DiffusionGemma 26B A4B IT accepts text and images; Qwen3.5 4B accepts text, images and video. Gemma 4 E2B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0 and Apache-2.0), so you can self-host them.
Which is newer?
DiffusionGemma 26B A4B IT is the newest, released Jun 9, 2026. Gemma 4 E2B IT came out Apr 2, 2026; Qwen3.5 4B came out Mar 2, 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.
