Gemma 4 E2B IT vs Schematron V2 Turbo vs Qwen3.5 4B
Too close to call on our weighted score (Qwen3.5 4B 78, Gemma 4 E2B IT 76, Schematron V2 Turbo 55). The right pick depends on what you value most.
GoogleGemma 4 E2B IT
76/100- ECI—
- Price$0.04 / $0.08
- Context131K
Inference.net
Schematron V2 Turbo
55/100- ECI—
- Price$0.03 / $0.15
- Context128K
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, Schematron V2 Turbo 55/100), so choose by what matters most for your work: Qwen3.5 4B on price and Qwen3.5 4B for long inputs. 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 · Schematron V2 Turbo $0.06 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 4BQwen3.5 4B 262,144 · Gemma 4 E2B IT 131,072 · Schematron V2 Turbo 128,000 tokens
- Widest inputsGemma 4 E2B IT and Qwen3.5 4BGemma 4 E2B IT: Text, Images, Audio · Schematron V2 Turbo: Text · Qwen3.5 4B: Text, Images, Video
- Self-hostingGemma 4 E2B IT and Qwen3.5 4BPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Gemma 4 E2B IT | Schematron V2 Turbo | Qwen3.5 4B |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 100 |
| Inputs & features | 30% | 70 | 0 | 70 |
| Context window | 20% | 25 | 24 | 37 |
| Overall | 100% | 76/100 | 55/100 | 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 | Schematron V2 TurboInference.net | 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 | $0.03 (best) | $0.04 |
| Output | $0.08 | $0.15 | $0.07 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.05 | $0.06 | $0.048 (best) |
| 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 | 131,072 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 8,192 tokens | 8,192 tokens | 81,920 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | Yes | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenApache-2.0 |
| API model ID | — | — | — |
| API providers | 1 | 5 (best) | 5 (best) |
| Released | Apr 2, 2026 | Apr 16, 2026 | Mar 2, 2026 |
| Knowledge cutoff | — | — | — |
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
- Schematron V2 Turbo$0.60
- Qwen3.5 4B$0.54
Which should you choose?
Which is better: Gemma 4 E2B IT, Schematron V2 Turbo 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, Schematron V2 Turbo 55/100), so choose by what matters most for your work: Qwen3.5 4B on price and Qwen3.5 4B for long inputs. 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, Schematron V2 Turbo 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); Schematron V2 Turbo costs $0.03 input / $0.15 output per million tokens (median across 5 API providers). 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 $0.06 for Schematron V2 Turbo (1.3× 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, Schematron V2 Turbo 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, Schematron V2 Turbo and Qwen3.5 4B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Schematron V2 Turbo does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen3.5 4B has the largest context window at 262,144 tokens, against 131,072 for Gemma 4 E2B IT and 128,000 for Schematron V2 Turbo. Maximum output per response: Gemma 4 E2B IT up to 8,192, Schematron V2 Turbo up to 8,192, 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; Schematron V2 Turbo accepts text; Qwen3.5 4B accepts text, images and video. Gemma 4 E2B IT handles the widest range of inputs.
Are any of these open source?
Gemma 4 E2B IT and Qwen3.5 4B publishes its weights (Apache-2.0) and can be self-hosted; Schematron V2 Turbo is proprietary.
Which is newer?
Schematron V2 Turbo is the newest, released Apr 16, 2026. Gemma 4 E2B IT came out Apr 2, 2026; Qwen3.5 4B came out Mar 2, 2026.
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.
