Gemma 4 E2B vs GLM-5
Gemma 4 E2B (2026) and GLM-5 (2026) are frontier reasoning models from Google DeepMind and Zhipu AI. Gemma 4 E2B ships a 128k-token context window, while GLM-5 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.
Gemma 4 E2B is safer overall; choose GLM-5 when reasoning depth matters.
Decision scorecard
Local evidence first| Signal | Gemma 4 E2B | GLM-5 |
|---|---|---|
| Best for | multimodal apps and tool-calling agents | reasoning-heavy apps, tool-calling agents, and provider-routed production |
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Agents |
| Context window | 128k | 200k |
| Cheapest output | - | $2.08/1M tokens |
| Provider routes | 1 tracked | 7 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.
- GLM-5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 has broader tracked provider coverage for fallback and procurement flexibility.
- GLM-5 uniquely exposes Reasoning, Tool use, and Structured outputs in local model data.
- Local decision data tags GLM-5 for Coding, RAG, and Agents.
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-5
$1,000
Cheapest tracked route/tier: OpenRouter
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on GCP Vertex AI; start route-level A/B tests there.
- Check replacement coverage for Multimodal before moving production traffic.
- GLM-5 adds Reasoning, Tool use, and Structured outputs in local capability data.
- Provider overlap exists on GCP Vertex AI; start route-level A/B tests there.
- Check replacement coverage for Reasoning, Tool use, and Structured outputs before moving production traffic.
- Gemma 4 E2B adds Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-31 | 2026-02-11 |
| Context window | 128k | 200k |
| Parameters | 2B | 744B total, 40B active |
| Architecture | - | mixture of experts |
| License | Open Source | MIT |
| Knowledge cutoff | 2025-01 | 2025-11 |
Pricing and availability
| Pricing attribute | Gemma 4 E2B | GLM-5 |
|---|---|---|
| Input price | - | $0.60/1M tokens |
| Output price | - | $2.08/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 4 E2B | GLM-5 |
|---|---|---|
| Vision | No | No |
| Multimodal | Yes | No |
| Reasoning | No | Yes |
| Function calling | Yes | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Gemma 4 E2B, reasoning mode: GLM-5, tool use: GLM-5, and structured outputs: GLM-5. 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 GLM-5 has $0.60/1M input tokens. Provider availability is 1 tracked routes versus 7. 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-5 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-5?
GLM-5 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-5 open source?
Gemma 4 E2B is listed under Open Source. GLM-5 is listed under MIT. 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, Gemma 4 E2B or GLM-5?
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 GLM-5?
GLM-5 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.
Which is better for function calling, Gemma 4 E2B or GLM-5?
Both Gemma 4 E2B and GLM-5 expose function calling. 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.
Where can I run Gemma 4 E2B and GLM-5?
Gemma 4 E2B is available on GCP Vertex AI. GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. 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.