LLM Reference

Gemma 4 E2B vs GLM-5V-Turbo

Gemma 4 E2B (2026) and GLM-5V-Turbo (2026) are frontier reasoning models from Google DeepMind and Zhipu AI. Gemma 4 E2B ships a 128k-token context window, while GLM-5V-Turbo ships a 200k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.

GLM-5V-Turbo is safer overall; choose Gemma 4 E2B when provider fit matters.

Decision scorecard

Local evidence first
SignalGemma 4 E2BGLM-5V-Turbo
Best formultimodal apps and tool-calling agentsreasoning-heavy apps, multimodal apps, and tool-calling agents
Decision fitRAG, Agents, and Long contextRAG, Agents, and Long context
Context window128k200k
Cheapest output-$4/1M tokens
Provider routes1 tracked2 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Gemma 4 E2B when...
  • Local decision data tags Gemma 4 E2B for RAG, Agents, and Long context.
Choose GLM-5V-Turbo when...
  • GLM-5V-Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • GLM-5V-Turbo has broader tracked provider coverage for fallback and procurement flexibility.
  • GLM-5V-Turbo uniquely exposes Vision, Reasoning, and Tool use in local model data.
  • Local decision data tags GLM-5V-Turbo for RAG, Agents, and Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Gemma 4 E2B

Unavailable

No complete token price in local provider data

GLM-5V-Turbo

$1,960

Cheapest tracked route/tier: OpenRouter

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Gemma 4 E2B -> GLM-5V-Turbo
  • No overlapping tracked provider route is sourced for Gemma 4 E2B and GLM-5V-Turbo; plan for SDK, billing, or endpoint changes.
  • GLM-5V-Turbo adds Vision, Reasoning, and Tool use in local capability data.
GLM-5V-Turbo -> Gemma 4 E2B
  • No overlapping tracked provider route is sourced for GLM-5V-Turbo and Gemma 4 E2B; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Vision, Reasoning, and Tool use before moving production traffic.

Specs

Specification
Released2026-03-312026-04-01
Context window128k200k
Parameters2B744B total, 40B active
Architecture-mixture of experts
LicenseApache 2.0Proprietary
Knowledge cutoff2025-012025-11

Pricing and availability

Pricing attributeGemma 4 E2BGLM-5V-Turbo
Input price-$1.20/1M tokens
Output price-$4/1M tokens
Providers

Capabilities

CapabilityGemma 4 E2BGLM-5V-Turbo
VisionNoYes
MultimodalYesYes
ReasoningNoYes
Function callingYesYes
Tool useNoYes
Structured outputsNoYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: GLM-5V-Turbo, reasoning mode: GLM-5V-Turbo, tool use: GLM-5V-Turbo, and structured outputs: GLM-5V-Turbo. Both models share multimodal input and 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 GLM-5V-Turbo has $1.20/1M 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 GLM-5V-Turbo when reasoning depth, 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Which has a larger context window, Gemma 4 E2B or GLM-5V-Turbo?

GLM-5V-Turbo supports 200k 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 GLM-5V-Turbo open source?

Gemma 4 E2B is listed under Apache 2.0. GLM-5V-Turbo 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 GLM-5V-Turbo?

GLM-5V-Turbo 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 GLM-5V-Turbo?

Both Gemma 4 E2B and GLM-5V-Turbo expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for reasoning mode, Gemma 4 E2B or GLM-5V-Turbo?

GLM-5V-Turbo 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 GLM-5V-Turbo?

Gemma 4 E2B is available on GCP Vertex AI. GLM-5V-Turbo is available on OpenRouter and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-06-03. Data sourced from public model cards and provider documentation.