GPT-4.1 vs GLM 5.3
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | GPT-4.1 | GLM-5.3 |
|---|---|---|
| CostLower is better · Published-token output estimate | UnrankedNot in the 36-model eligible cohort | #28 of 36$0.248 per LiveBench case |
Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
| Benchmark | GPT-4.1 | GLM-5.3 |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,382.7194% of row best · rating · gpt-4.1-2025-04-14; 95% CI [1379.00467679, 1386.41941365]; votes 49913; rank 153 | 1,474.49100% of row best · rating · glm-5.3-max; 95% CI [1467.12172899, 1481.86064533]; votes 7401; rank 18 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 1 benchmark winOverall 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-4.1 | GLM-5.3 |
|---|---|---|
| Developer | OpenAI | Z.ai |
| Family | Gpt 4 1 | Glm 5 3 |
| Model | GPT-4.1 | GLM-5.3 |
| Version | GPT-4.1 | GLM-5.3 |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | 2024-06-01 | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,048K | 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, tools | agents, chat, reasoning, structured_outputs, tools |
13 comparable fields · 8 material differences · Pair passes the primary-source comparison gate
GPT-4.1 Capabilities
GLM 5.3 Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
GPT-4.1 vs GLM 5.3 FAQs
Is GPT-4.1 or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-4.1 and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-4.1 or GLM 5.3?+
GPT-4.1 is $1.00 and GLM 5.3 is $1.20 per million tokens, so GPT-4.1 is cheaper on this metric. GPT-4.1 is $4.00 and GLM 5.3 is $4.00 per million tokens, so they are tied on this metric.
Which has a larger context window, GPT-4.1 or GLM 5.3?+
GPT-4.1 has the larger sourced context window. GPT-4.1 supports 1,048K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, GPT-4.1 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-4.1 or GLM 5.3 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-4.1 is not marked open weight; GLM 5.3 is not marked open weight.
Can GPT-4.1 and GLM 5.3 understand images?+
GPT-4.1 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-4.1 or GLM 5.3?+
GLM 5.3 has the larger sourced maximum output: GPT-4.1 supports 33K and GLM 5.3 supports 131K output tokens.
Do GPT-4.1 and GLM 5.3 support reasoning and tool use?+
GPT-4.1: 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-4.1 or GLM 5.3?+
GPT-4.1 has 2 sourced provider routes; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.
Which offers better value, GPT-4.1 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.