Qwen3.5 2B vs GPT-5.3 Chat vs Gemma 4 E4B IT
Too close to call on our weighted score (Qwen3.5 2B 78, Gemma 4 E4B IT 76, GPT-5.3 Chat 28). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3.5 2B
78/100- ECI—
- Price$0.02 / $0.10
- Context262K
OpenAIGPT-5.3 Chat
28/100- ECI—
- Price$1.75 / $14.00
- Context128K
GoogleGemma 4 E4B IT
76/100- ECI—
- Price$0.02 / $0.10
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3.5 2B 78/100, Gemma 4 E4B IT 76/100, GPT-5.3 Chat 28/100), so choose by what matters most for your work: Qwen3.5 2B on price and Qwen3.5 2B 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 2B and Gemma 4 E4B ITQwen3.5 2B $0.04 · Gemma 4 E4B IT $0.04 · GPT-5.3 Chat $4.81 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 2BQwen3.5 2B 262,144 · Gemma 4 E4B IT 131,072 · GPT-5.3 Chat 128,000 tokens
- Widest inputsQwen3.5 2B and Gemma 4 E4B ITQwen3.5 2B: Text, Images, Video · GPT-5.3 Chat: Text, Images · Gemma 4 E4B IT: Text, Images, Audio
- Self-hostingQwen3.5 2B and Gemma 4 E4B ITPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Qwen3.5 2B | GPT-5.3 Chat | Gemma 4 E4B IT |
|---|---|---|---|---|
| Price | 50% | 100 | 17 | 100 |
| Inputs & features | 30% | 70 | 50 | 70 |
| Context window | 20% | 37 | 24 | 25 |
| Overall | 100% | 78/100 | 28/100 | 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 | Qwen3.5 2BAlibaba (Qwen) | GPT-5.3 ChatOpenAI | Gemma 4 E4B ITGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.02 (best) | $1.75 | $0.02 (best) |
| Output | $0.10 (best) | $14.00 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.04 (best) | $4.81 | $0.04 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 4 providers | Median of 1 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 128,000 tokens | 131,072 tokens |
| Max output | 81,920 tokens (best) | 16,384 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | Yes | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache-2.0 | Proprietary | Open |
| API model ID | — | — | — |
| API providers | 2 | 4 (best) | 2 |
| Released | Mar 2, 2026 | Mar 3, 2026 | Apr 2, 2026 |
| Knowledge cutoff | — | Aug 31, 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3.5 2B$0.40
- GPT-5.3 Chat$45.50
- Gemma 4 E4B IT$0.40
Which should you choose?
Which is better: Qwen3.5 2B, GPT-5.3 Chat or Gemma 4 E4B IT?
It is close. Our weighted score puts them within 2 points (Qwen3.5 2B 78/100, Gemma 4 E4B IT 76/100, GPT-5.3 Chat 28/100), so choose by what matters most for your work: Qwen3.5 2B on price and Qwen3.5 2B 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, Qwen3.5 2B, GPT-5.3 Chat or Gemma 4 E4B IT?
Qwen3.5 2B is cheaper at $0.02 input / $0.10 output per million tokens (median across 1 API provider). Gemma 4 E4B IT costs $0.02 input / $0.10 output per million tokens (median across 1 API provider); GPT-5.3 Chat costs $1.75 input / $14.00 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $0.04 per million tokens for Qwen3.5 2B versus $0.04 for Gemma 4 E4B IT (1× as much) and $4.81 for GPT-5.3 Chat (120× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.5 2B has not been scored yet, GPT-5.3 Chat has not been scored yet and Gemma 4 E4B IT has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 2B, GPT-5.3 Chat and Gemma 4 E4B IT 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?
Qwen3.5 2B has the largest context window at 262,144 tokens, against 131,072 for Gemma 4 E4B IT and 128,000 for GPT-5.3 Chat. Maximum output per response: Qwen3.5 2B up to 81,920, GPT-5.3 Chat up to 16,384, Gemma 4 E4B IT up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Qwen3.5 2B accepts text, images and video; GPT-5.3 Chat accepts text and images; Gemma 4 E4B IT accepts text, images and audio. Qwen3.5 2B handles the widest range of inputs.
Are any of these open source?
Qwen3.5 2B and Gemma 4 E4B IT publishes its weights (Apache-2.0) and can be self-hosted; GPT-5.3 Chat is proprietary.
Which is newer?
Gemma 4 E4B IT is the newest, released Apr 2, 2026. GPT-5.3 Chat came out Mar 3, 2026; Qwen3.5 2B came out Mar 2, 2026. Knowledge cutoff: GPT-5.3 Chat Aug 31, 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.
