Hy4 preview vs GLM-5.3
Why this pair: Current Tencent and Z.ai frontier language models
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Hy4 preview | GLM-5.3 |
|---|---|---|
| Developer | Tencent | Z.ai |
| Family | Hy4 | Glm 5 3 |
| Model | Hy4 preview | GLM-5.3 |
| Version | Hy4 preview | GLM-5.3 |
| Lifecycle | preview | active |
| Released | 2026-08-28 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 1,000,000 | 1,000,000 |
| Total parameters | 770,000,000,000 | Unknown |
| Active parameters | 49,000,000,000 | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | No | Yes |
| Self-hostable | Yes | No |
| Provider access | Openrouter (Standard) | Deepinfra (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, reasoning, structured_outputs, tools |
13 comparable fields · 10 material differences · Editorially curated pair passes the primary-source comparison gate
Hy4 preview Capabilities
GLM-5.3 Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
Hy3Tencent | vs | Hy4 previewTencent | family variantstext |
Claude Fable 5.1Anthropic | vs | Hy4 previewTencent | cross-developer peerstext |
 GPT-5.6 SolOpenAI | vs | Hy4 previewTencent | cross-developer peerstext |
 Gemini 3.1 ProGoogle DeepMind | vs | Hy4 previewTencent | cross-developer peerstext |
 DeepSeek-V4-ProDeepSeek | vs | Hy4 previewTencent | cross-developer peerstext |
| vs | Hy4 previewTencent | cross-developer peerstext | |
 Kimi-K3Moonshot AI | vs | Hy4 previewTencent | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Hy4 previewTencent | cross-developer peerstext |
| vs | Hy4 previewTencent | cross-developer peerstext | |
 Mistral Large 3Mistral AI | vs | Hy4 previewTencent | cross-developer peerstext |
 Qwen3.7-PlusQwen | vs | GLM-5.3Z.ai | cross-developer peerstext |
GLM-5.3Z.ai | vs | GLM-5.3-FlashZ.ai | family variantstext |
Primary Evidence
Sources and Freshness
Questions
Hy4 preview vs GLM-5.3 FAQs
Is Hy4 preview or GLM-5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Hy4 preview and GLM-5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Hy4 preview or GLM-5.3?+
Hy4 preview is $0.834 and GLM-5.3 is $1.20 per million tokens, so Hy4 preview is cheaper on this metric. Hy4 preview is $2.501 and GLM-5.3 is $4.00 per million tokens, so Hy4 preview is cheaper on this metric.
Which has a larger context window, Hy4 preview or GLM-5.3?+
Neither model has a larger sourced context window in this comparison. Hy4 preview is 1,000,000 and GLM-5.3 is 1,000,000.
Which performs better in benchmarks, Hy4 preview or GLM-5.3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Hy4 preview or GLM-5.3 be self-hosted?+
Hy4 preview is the only model in this pair currently marked as self-hostable. Hy4 preview is open weight; GLM-5.3 is not marked open weight.
Can Hy4 preview and GLM-5.3 understand images?+
Hy4 preview is not documented with image input; GLM-5.3 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Hy4 preview or GLM-5.3?+
Neither has a larger sourced maximum output. Hy4 preview is — and GLM-5.3 is 131,072.
Do Hy4 preview and GLM-5.3 support reasoning and tool use?+
Hy4 preview: reasoning and tool calling. GLM-5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Hy4 preview or GLM-5.3?+
Hy4 preview has 1 sourced provider route; GLM-5.3 has 2, so GLM-5.3 has broader tracked availability.
Which offers better value, Hy4 preview or GLM-5.3?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.