LLM ReferenceLLM Reference

GLM-5 vs Phi-4 Mini Flash Reasoning

GLM-5 (2026) and Phi-4 Mini Flash Reasoning (2025) are frontier-tier reasoning models from Zhipu AI and Microsoft Research. GLM-5 ships a 200k-token context window, while Phi-4 Mini Flash Reasoning 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.

GLM-5 is safer overall; choose Phi-4 Mini Flash Reasoning when provider fit matters.

Specs

Released2026-02-112025-12-01
Context window200k128K
Parameters744B total, 40B active
Architecturemixture of expertsdecoder only
LicenseMIT1
Knowledge cutoff--

Pricing and availability

GLM-5Phi-4 Mini Flash Reasoning
Input price$0.72/1M tokens-
Output price$2.3/1M tokens-
Providers

Capabilities

GLM-5Phi-4 Mini Flash Reasoning
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on function calling: GLM-5, tool use: GLM-5, and structured outputs: GLM-5. Both models share reasoning mode, 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: GLM-5 has $0.72/1M input tokens and Phi-4 Mini Flash Reasoning has no token price sourced yet. Provider availability is 5 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose GLM-5 when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Phi-4 Mini Flash Reasoning 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. 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, GLM-5 or Phi-4 Mini Flash Reasoning?

GLM-5 supports 200k tokens, while Phi-4 Mini Flash Reasoning supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is GLM-5 or Phi-4 Mini Flash Reasoning open source?

GLM-5 is listed under MIT. Phi-4 Mini Flash Reasoning is listed under 1. 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, GLM-5 or Phi-4 Mini Flash Reasoning?

Both GLM-5 and Phi-4 Mini Flash Reasoning expose reasoning mode. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Which is better for function calling, GLM-5 or Phi-4 Mini Flash Reasoning?

GLM-5 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 tool use, GLM-5 or Phi-4 Mini Flash Reasoning?

GLM-5 has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run GLM-5 and Phi-4 Mini Flash Reasoning?

GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Phi-4 Mini Flash Reasoning is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.