Choosing between GLM 5.2 and Microsoft's Phi-4 comes down to a single question: do you need frontier-grade reasoning over massive context, or a capable model that ships on a laptop? GLM 5.2 is a 753B Mixture-of-Experts model built for complex software engineering and long-document workflows; Phi-4 is a 14B dense model that fits in 8–16 GB of VRAM and runs free on commodity hardware. The right answer depends on your workload, your budget, and where your data can travel.
Quick Comparison
| Dimension | GLM 5.2 | Phi-4 |
|---|---|---|
| Architecture | 753B MoE | 14B dense |
| Context window | 1M tokens | 16K–128K tokens (variant-dependent) |
| SWE-bench Pro | 62.1% | not publicly benchmarked |
| License | Proprietary API | MIT (fully open) |
| Self-host hardware | Not practical for most teams | 8–16 GB VRAM (single consumer GPU) |
| Primary availability | glm5.app, ZhipuAI API | Ollama, Azure AI Foundry, HuggingFace |
| Modalities | Text, vision, long-context code | Text, math, code |
| Self-host cost | N/A | Free (your compute only) |
Benchmark Performance
| Benchmark | GLM 5.2 | Phi-4 |
|---|---|---|
| SWE-bench Pro (coding) | 62.1% | not publicly benchmarked |
| MATH (competition math) | not publicly benchmarked | ~80%+ (14B class-leading) |
| MMLU (general knowledge) | not publicly benchmarked | ~84% |
| HumanEval (code) | not publicly benchmarked | ~82% |
GLM 5.2's headline number is SWE-bench Pro at 62.1%, which places it among the top tier of models for real-world software engineering tasks — resolving GitHub issues, writing patches, and navigating large codebases. This is where the 1M-token context window pays off directly: an agent can ingest an entire repository in a single pass.
Phi-4 tells a different story. For a 14B-parameter model it achieves remarkably high scores on mathematical reasoning and instruction-following benchmarks, often beating models two to three times its size. If your workload is structured problem-solving — tutoring, step-by-step reasoning, or local Q&A over documents — Phi-4 punches well above its weight class.
The gap that matters most in practice is context length. At 1M tokens, GLM 5.2 can hold roughly 750,000 words in a single prompt — enough for entire codebases or legal document corpora. Phi-4's maximum of 128K tokens (on supported variants) is generous for most tasks but is a hard ceiling for large-scale retrieval or multi-file analysis.
Pricing Breakdown
Pricing figures below are approximate and subject to change; verify current rates on provider pages before budgeting.
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Self-host |
|---|---|---|---|
| GLM 5.2 (ZhipuAI API) | ~$0.14 | ~$0.42 | Not practical |
| Phi-4 (Azure AI Foundry) | ~$0.07 | ~$0.14 | Free via Ollama |
| glm5.app | Free (web) | Free (web) | — |
Concrete example — 5,000 requests/day, 50K input + 30K output tokens each:
Daily token volume: 250M input tokens, 150M output tokens.
- GLM 5.2 via API: (250M × $0.14/1M) + (150M × $0.42/1M) = $35 + $63 = $98/day → ~$35,770/year
- Phi-4 via Azure: (250M × $0.07/1M) + (150M × $0.14/1M) = $17.50 + $21 = $38.50/day → ~$14,053/year
- Phi-4 via Ollama (self-hosted): Hardware and electricity only — roughly $0 per token at scale once the GPU is paid for.
The annualized gap between GLM 5.2 and hosted Phi-4 at this volume is roughly $21,700/year. For teams that can self-host, Phi-4 via Ollama reduces ongoing API costs to near zero — a compelling case for latency-tolerant, privacy-sensitive applications.
Context Window and Long-Document Tasks
GLM 5.2's 1M-token context is genuinely differentiated. Most competing models top out at 200K tokens; a 1M window lets you load full Git repositories, lengthy legal contracts, or multi-year financial filings without chunking or retrieval augmentation. For agentic coding tasks this matters enormously — the model sees all the imports, tests, and dependency files at once.
Phi-4's 128K-token ceiling covers the majority of everyday developer tasks: long PDFs, moderate-sized codebases, and multi-turn conversations. Where it falls short is repository-scale analysis and cross-file refactoring sessions that exceed that ceiling.
Open Source and Licensing
Phi-4 ships under the MIT license. You can download the weights from Hugging Face today, fine-tune them on your own data, and deploy them in air-gapped environments with no ongoing licensing obligations. This is significant for regulated industries (healthcare, finance, government) where sending data to an external API is not an option.
GLM 5.2 is accessible via API only. The weights are not publicly released. Teams that need on-premises deployment or full model auditability cannot use GLM 5.2 — Phi-4 is the only viable path in those scenarios.
Sample API Usage
# GLM 5.2 — OpenAI-compatible endpoint (ZhipuAI)
from openai import OpenAI
glm = OpenAI(
base_url="https://open.bigmodel.cn/api/paas/v4/",
api_key="YOUR_ZHIPUAI_KEY",
)
glm_resp = glm.chat.completions.create(
model="glm-4-plus", # use the latest GLM 5.2 identifier
messages=[{"role": "user", "content": "Refactor this function: ..."}],
)
print(glm_resp.choices[0].message.content)
# Phi-4 — local via Ollama (no API key required)
import requests, json
resp = requests.post(
"http://localhost:11434/api/generate",
json={"model": "phi4", "prompt": "Refactor this function: ...", "stream": False},
)
print(json.loads(resp.text)["response"])When to Choose GLM 5.2
- You need to process codebases, legal corpora, or document sets that exceed 128K tokens in a single pass.
- Your use case is complex, multi-file software engineering — SWE-bench Pro 62.1% is a meaningful signal for agentic code tasks.
- You want a managed API with no infrastructure overhead and can accept the higher per-token cost.
- Your application relies on vision input alongside long text context.
- You are prototyping quickly and want free access right now via glm5.app/chat without configuring an API key.
- Production throughput and MoE efficiency at scale are priorities over self-hosting flexibility.
- You need a model that handles multilingual tasks robustly, especially Chinese-English cross-lingual workloads.
When to Choose Phi-4
- Your team needs to run inference on-premises with no data leaving your network.
- Hardware budget is limited — a single consumer GPU with 8–16 GB VRAM is sufficient.
- Your workload is math-heavy, structured reasoning, or step-by-step tutoring where Phi-4's benchmark profile shines.
- You want an MIT-licensed model you can fine-tune freely on proprietary data.
- Context windows up to 128K are sufficient for your documents and workflows.
- You are optimizing for the lowest possible inference cost at high request volumes.
- Edge deployment or mobile inference is a future requirement — a 14B model is far more portable.
Frequently Asked Questions
Which model is better overall? Neither is universally better. GLM 5.2 leads on complex coding and long-context tasks backed by its 1M-token window and SWE-bench Pro score. Phi-4 leads on portability, cost, and openness. The best model is the one that fits your hardware, budget, and compliance requirements.
How large is the cost difference? At the example workload of 5,000 requests/day, GLM 5.2 via API runs roughly $35,770/year versus $14,053/year for hosted Phi-4 — about 2.5× more expensive. If you self-host Phi-4 on existing GPU hardware the API cost drops to near zero.
What is the biggest capability gap? Context length is the clearest gap: GLM 5.2 supports 1M tokens; Phi-4 tops out at 128K. The second gap is complex software engineering — GLM 5.2's 62.1% SWE-bench Pro score has no published Phi-4 equivalent to compare against.
Can I switch between them mid-project?
Both expose OpenAI-compatible chat APIs, so switching is mostly a one-line change to model and base_url. The practical friction is prompt tuning — each model has different instruction-following strengths — and output length expectations. Expect a calibration pass when migrating critical workflows.
Is Phi-4 truly free? The weights are MIT-licensed and free to download. Running them requires GPU hardware; via Ollama on your own machine there are no per-token charges. Azure AI Foundry hosting incurs the pay-per-token rates shown above.
Does GLM 5.2 support vision? Yes. GLM 5.2 supports vision inputs alongside text, making it suitable for multi-modal document analysis workflows. Phi-4's base model is text and code focused; multi-modal variants exist separately.
Bottom Line
GLM 5.2 is the right choice when you need frontier-scale reasoning, 1M-token context, or top-tier coding performance and can accept API-only access and higher costs. Phi-4 is the right choice when you need a capable, MIT-licensed model that runs on a single GPU, costs a fraction as much to operate, and can live entirely within your own infrastructure. Most teams will find GLM 5.2 valuable for the hardest tasks and Phi-4 indispensable for high-volume, cost-sensitive, or air-gapped workloads.
Try GLM 5.2 — no API key needed: glm5.app/chat
Sources
- Artificial Analysis model comparison leaderboard: https://artificialanalysis.ai/models
- Microsoft Phi-4 model card (Hugging Face): https://huggingface.co/microsoft/phi-4
- ZhipuAI GLM API pricing: https://open.bigmodel.cn/pricing
- Phi-4 on Azure AI Foundry: https://ai.azure.com/explore/models/Phi-4/version/4/registry/azureml
- Phi-4 via Ollama model library: https://ollama.com/library/phi4