GLM 5.3 vs GLM 5V Turbo
At a Glance
| Compare |  GLM 5.3Z.ai | GLM 5V TurboZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #16 of 4670.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.9–80.3 | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #34 of 44$0.248 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #26 of 3850.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 38.2–54.9 | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.20Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $4.00Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,000K | 200K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | GLM-5.3 | GLM-5V-Turbo |
|---|---|---|
| Developer | Z.ai | Z.ai |
| Family | Glm 5 3 | Glm 5v |
| Model | GLM-5.3 | GLM-5V-Turbo |
| Version | GLM-5.3 | GLM-5V-Turbo |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 1,000K | 200K |
| 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 | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) | Z.ai (Standard) |
| Capabilities | agents, chat, reasoning, structured_outputs, tools | agents, chat, computer-use, reasoning, tools, vision |
GLM 5.3 Capabilities
GLM 5V Turbo Capabilities
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Primary Evidence
Sources and Freshness
Questions
GLM 5.3 vs GLM 5V Turbo FAQs
Is GLM 5.3 or GLM 5V Turbo better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GLM 5.3 and GLM 5V Turbo, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GLM 5.3 or GLM 5V Turbo?+
Only GLM 5.3 has a directly sourced input price: $1.20 per million tokens. Only GLM 5.3 has a directly sourced output price: $4.00 per million tokens.
Which has a larger context window, GLM 5.3 or GLM 5V Turbo?+
GLM 5.3 has the larger sourced context window. GLM 5.3 supports 1,000K and GLM 5V Turbo supports 200K.
Which performs better in benchmarks, GLM 5.3 or GLM 5V Turbo?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can GLM 5.3 or GLM 5V Turbo be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GLM 5.3 is not marked open weight; GLM 5V Turbo is not marked open weight.
Can GLM 5.3 and GLM 5V Turbo understand images?+
GLM 5.3 is not documented with image input; GLM 5V Turbo is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GLM 5.3 or GLM 5V Turbo?+
Neither has a larger sourced maximum output. GLM 5.3 is 131K and GLM 5V Turbo is 131K.
Do GLM 5.3 and GLM 5V Turbo support reasoning and tool use?+
GLM 5.3: reasoning and tool calling. GLM 5V Turbo: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GLM 5.3 or GLM 5V Turbo?+
GLM 5.3 has 4 sourced provider routes; GLM 5V Turbo has 1, so GLM 5.3 has broader tracked availability.
Which offers better value, GLM 5.3 or GLM 5V Turbo?+
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.