GPT-5.4 Pro vs GLM-5.3
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | GPT-5.4 Pro | GLM-5.3 |
|---|---|---|
| Developer | OpenAI | Z.ai |
| Family | Gpt 5 4 | Glm 5 3 |
| Model | GPT-5.4 Pro | GLM-5.3 |
| Version | GPT-5.4 Pro | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2026-03-05 | Unknown |
| Knowledge cutoff | 2025-08-31 | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,050,000 | 1,000,000 |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Openai (Standard), Openrouter (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | agents, chat, reasoning, structured_outputs, tools |
13 comparable fields · 8 material differences · Pair passes the primary-source comparison gate
GPT-5.4 Pro Capabilities
GLM-5.3 Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
 Hy4 previewTencent | vs | GLM-5.3Z.ai | cross-developer peerstext |
GPT-5.4 ProOpenAI | vs | GPT-5.5OpenAI | family variantsimage, text |
GPT-5.4 ProOpenAI | vs | GPT-5.5 ProOpenAI | family variantsimage, text |
Claude Opus 4.6Anthropic | vs | GPT-5.4 ProOpenAI | cross-developer peersimage, text |
Claude Opus 4.7Anthropic | vs | GPT-5.4 ProOpenAI | cross-developer peersimage, text |
GPT-5.4 ProOpenAI | vs | cross-developer peersimage, text | |
GPT-5.4 ProOpenAI | vs | GPT-5.6 LunaOpenAI | family variantsimage, text |
GPT-5.4 ProOpenAI | vs | GPT-5.6 TerraOpenAI | family variantsimage, text |
Claude Sonnet 5Anthropic | vs | GPT-5.4 ProOpenAI | cross-developer peersimage, text |
GPT-5.4 ProOpenAI | vs | Grok 4.6xAI | cross-developer peersimage, text |
 Qwen3.7-PlusQwen | vs | GLM-5.3Z.ai | cross-developer peerstext |
Qwen3.8-FlashQwen | vs | GLM-5.3Z.ai | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
GPT-5.4 Pro vs GLM-5.3 FAQs
Is GPT-5.4 Pro or GLM-5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.4 Pro and GLM-5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-5.4 Pro or GLM-5.3?+
GPT-5.4 Pro is $30.00 and GLM-5.3 is $1.20 per million tokens, so GLM-5.3 is cheaper on this metric. GPT-5.4 Pro is $180.00 and GLM-5.3 is $4.00 per million tokens, so GLM-5.3 is cheaper on this metric.
Which has a larger context window, GPT-5.4 Pro or GLM-5.3?+
GPT-5.4 Pro has the larger sourced context window. GPT-5.4 Pro supports 1,050,000 and GLM-5.3 supports 1,000,000.
Which performs better in benchmarks, GPT-5.4 Pro 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 GPT-5.4 Pro or GLM-5.3 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-5.4 Pro is not marked open weight; GLM-5.3 is not marked open weight.
Can GPT-5.4 Pro and GLM-5.3 understand images?+
GPT-5.4 Pro is 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, GPT-5.4 Pro or GLM-5.3?+
GLM-5.3 has the larger sourced maximum output: GPT-5.4 Pro supports 128,000 and GLM-5.3 supports 131,072 output tokens.
Do GPT-5.4 Pro and GLM-5.3 support reasoning and tool use?+
GPT-5.4 Pro: reasoning, tool calling, and image input. GLM-5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-5.4 Pro or GLM-5.3?+
GPT-5.4 Pro has 2 sourced provider routes; GLM-5.3 has 4, so GLM-5.3 has broader tracked availability.
Which offers better value, GPT-5.4 Pro 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.