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DeepSeek V3.2 vs Gemma 4 E2B

DeepSeek V3.2 (2025) and Gemma 4 E2B (2026) are compact production models from DeepSeek and Google DeepMind. DeepSeek V3.2 ships a 160K-token context window, while Gemma 4 E2B ships a 128k-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 DeepSeek V3.2 when coding workflow support matters.

Specs

Specification
Released2025-01-012026-03-31
Context window160K128k
Parameters671B2B
Architecturedecoder only-
LicenseOpen SourceOpen Source
Knowledge cutoff--

Pricing and availability

Pricing attributeDeepSeek V3.2Gemma 4 E2B
Input price$0.25/1M tokens-
Output price$0.38/1M tokens-
Providers

Capabilities

CapabilityDeepSeek V3.2Gemma 4 E2B
VisionNoNo
MultimodalNoYes
ReasoningNoNo
Function callingNoYes
Tool useNoNo
Structured outputsYesNo
Code executionYesNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on multimodal input: Gemma 4 E2B, function calling: Gemma 4 E2B, structured outputs: DeepSeek V3.2, and code execution: DeepSeek V3.2. Both models share the core language-model surface, 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: DeepSeek V3.2 has $0.25/1M input tokens and Gemma 4 E2B has no token price sourced yet. Provider availability is 4 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose DeepSeek V3.2 when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Gemma 4 E2B when provider fit 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, DeepSeek V3.2 or Gemma 4 E2B?

DeepSeek V3.2 supports 160K 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 DeepSeek V3.2 or Gemma 4 E2B open source?

DeepSeek V3.2 is listed under Open Source. Gemma 4 E2B is listed under Open Source. 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 multimodal input, DeepSeek V3.2 or Gemma 4 E2B?

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 function calling, DeepSeek V3.2 or Gemma 4 E2B?

Gemma 4 E2B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for structured outputs, DeepSeek V3.2 or Gemma 4 E2B?

DeepSeek V3.2 has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run DeepSeek V3.2 and Gemma 4 E2B?

DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, and OpenRouter. Gemma 4 E2B is available on GCP Vertex AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

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Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.