Gemini Deep Research vs GLM-5
Gemini Deep Research (2024) and GLM-5 (2026) are frontier reasoning models from Google DeepMind and Zhipu AI. Gemini Deep Research 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.
GLM-5 is safer overall; choose Gemini Deep Research when provider fit matters.
Decision scorecard
Local evidence first| Signal | Gemini Deep Research | GLM-5 |
|---|---|---|
| Best for | 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
- Local decision data tags Gemini Deep Research 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 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.
Gemini Deep Research
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
- No overlapping tracked provider route is sourced for Gemini Deep Research and GLM-5; plan for SDK, billing, or endpoint changes.
- GLM-5 adds Reasoning in local capability data.
- No overlapping tracked provider route is sourced for GLM-5 and Gemini Deep Research; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-11 | 2026-02-11 |
| Context window | 128k | 200k |
| Parameters | — | 744B total, 40B active |
| Architecture | decoder only | mixture of experts |
| License | Proprietary | MIT(OSI) |
| Openness | Proprietary | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2025-01 | 2025-11 |
Pricing and availability
| Pricing attribute | Gemini Deep Research | GLM-5 |
|---|---|---|
| Input price | - | $0.60/1M tokens |
| Output price | - | $2.08/1M tokens |
| Providers |
Capabilities
| Capability | Gemini Deep Research | GLM-5 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | 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 reasoning mode: GLM-5. Both models share function calling, tool use, and structured outputs, 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: Gemini Deep Research 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 Gemini Deep Research 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, Gemini Deep Research or GLM-5?
GLM-5 supports 200k tokens, while Gemini Deep Research supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemini Deep Research or GLM-5 open source?
Gemini Deep Research is listed under Proprietary. 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 reasoning mode, Gemini Deep Research 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, Gemini Deep Research or GLM-5?
Both Gemini Deep Research 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.
Which is better for tool use, Gemini Deep Research or GLM-5?
Both Gemini Deep Research and GLM-5 expose tool use. 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 Gemini Deep Research and GLM-5?
Gemini Deep Research is available on Google AI Studio. 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: 2026-05-22. Data sourced from public model cards and provider documentation.