Gemma 4 E2B vs GPT-5.3-Codex
Gemma 4 E2B (2026) and GPT-5.3-Codex (2026) are agentic coding models from Google DeepMind and OpenAI. Gemma 4 E2B ships a 128k-token context window, while GPT-5.3-Codex ships a 400K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
Gemma 4 E2B is safer overall; choose GPT-5.3-Codex when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | Gemma 4 E2B | GPT-5.3-Codex |
|---|---|---|
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Agents |
| Context window | 128k | 400K |
| Cheapest output | - | $14/1M tokens |
| Provider routes | 1 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 4 E2B uniquely exposes Multimodal in local model data.
- Local decision data tags Gemma 4 E2B for RAG, Agents, and Long context.
- GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-5.3-Codex uniquely exposes Vision, Reasoning, and Tool use in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 4 E2B
Unavailable
No complete token price in local provider data
GPT-5.3-Codex
$4,900
Cheapest tracked route: OpenRouter
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemma 4 E2B and GPT-5.3-Codex; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Multimodal before moving production traffic.
- GPT-5.3-Codex adds Vision, Reasoning, and Tool use in local capability data.
- No overlapping tracked provider route is sourced for GPT-5.3-Codex and Gemma 4 E2B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Reasoning, and Tool use before moving production traffic.
- Gemma 4 E2B adds Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-31 | 2026-02-05 |
| Context window | 128k | 400K |
| Parameters | 2B | — |
| Architecture | - | decoder only |
| License | Open Source | Proprietary |
| Knowledge cutoff | - | 2025-08 |
Pricing and availability
| Pricing attribute | Gemma 4 E2B | GPT-5.3-Codex |
|---|---|---|
| Input price | - | $1.75/1M tokens |
| Output price | - | $14/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 4 E2B | GPT-5.3-Codex |
|---|---|---|
| Vision | No | Yes |
| Multimodal | Yes | No |
| Reasoning | No | Yes |
| Function calling | Yes | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | Yes |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GPT-5.3-Codex, multimodal input: Gemma 4 E2B, reasoning mode: GPT-5.3-Codex, tool use: GPT-5.3-Codex, structured outputs: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share function calling, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: Gemma 4 E2B has no token price sourced yet and GPT-5.3-Codex has $1.75/1M input tokens. Provider availability is 1 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 4 E2B when provider fit are central to the workload. Choose GPT-5.3-Codex when coding workflow support, larger context windows, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, Gemma 4 E2B or GPT-5.3-Codex?
GPT-5.3-Codex supports 400K tokens, while Gemma 4 E2B supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 4 E2B or GPT-5.3-Codex open source?
Gemma 4 E2B is listed under Open Source. GPT-5.3-Codex is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, Gemma 4 E2B or GPT-5.3-Codex?
GPT-5.3-Codex has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Gemma 4 E2B or GPT-5.3-Codex?
Gemma 4 E2B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for reasoning mode, Gemma 4 E2B or GPT-5.3-Codex?
GPT-5.3-Codex has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Gemma 4 E2B and GPT-5.3-Codex?
Gemma 4 E2B is available on GCP Vertex AI. GPT-5.3-Codex is available on OpenRouter and OpenAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.