GPT-5.4 Mini vs GLM 5.3
Benchmark Performance
Available Benchmarks
| Benchmark | GPT-5.4 Mini | GLM-5.3 |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,412.1096% of row best · rating · gpt-5.4-mini-high; 95% CI [1408.28461201, 1415.92470838]; votes 59472; rank 123 | 1,474.49100% of row best · rating · glm-5.3-max; 95% CI [1467.12172899, 1481.86064533]; votes 7401; rank 18 |
| LiveBench2026-06-25 · overall · leader | 69.5388% of row best · percent · gpt-5.4-mini-xhigh · 47,448 output tokens / case | 79.15100% of row best · percent · glm-5.3 · 62,090 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 2 benchmark winsOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | GPT-5.4 Mini | GLM-5.3 |
|---|---|---|
| Developer | OpenAI | Z.ai |
| Family | Gpt 5 4 | Glm 5 3 |
| Model | GPT-5.4 Mini | GLM-5.3 |
| Version | GPT-5.4 Mini | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2026-03-17 | Unknown |
| Knowledge cutoff | 2025-08-31 | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 400K | 1,000K |
| 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 Mini Capabilities
GLM 5.3 Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
GPT-5.4 Mini vs GLM 5.3 FAQs
Is GPT-5.4 Mini or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.4 Mini 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 Mini or GLM 5.3?+
GPT-5.4 Mini is $0.75 and GLM 5.3 is $1.20 per million tokens, so GPT-5.4 Mini is cheaper on this metric. GPT-5.4 Mini is $4.50 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 Mini or GLM 5.3?+
GLM 5.3 has the larger sourced context window. GPT-5.4 Mini supports 400K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, GPT-5.4 Mini or GLM 5.3?+
GLM 5.3 leads the current overall benchmark count. The result uses 2 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can GPT-5.4 Mini or GLM 5.3 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-5.4 Mini is not marked open weight; GLM 5.3 is not marked open weight.
Can GPT-5.4 Mini and GLM 5.3 understand images?+
GPT-5.4 Mini 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 Mini or GLM 5.3?+
GLM 5.3 has the larger sourced maximum output: GPT-5.4 Mini supports 128K and GLM 5.3 supports 131K output tokens.
Do GPT-5.4 Mini and GLM 5.3 support reasoning and tool use?+
GPT-5.4 Mini: 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 Mini or GLM 5.3?+
GPT-5.4 Mini 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 Mini 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.