GPT-5.6 Luna vs GLM 5V Turbo
At a Glance
| Compare |  GPT-5.6 LunaOpenAI |  GLM 5V TurboZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #31 of 4652.7 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #6 of 44$0.026 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #4 of 3864.8 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.20Openai ↗ · Sep 3, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $1.20Openai ↗ · Sep 3, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,050K | 200K |
| Model facts checked | Aug 28, 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 | GPT-5.6 Luna | GLM-5V-Turbo |
|---|---|---|
| Developer | OpenAI | Z.ai |
| Family | Gpt 5 6 | Glm 5v |
| Model | GPT-5.6 Luna | GLM-5V-Turbo |
| Version | GPT-5.6 Luna | GLM-5V-Turbo |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | 2026-02-16 | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 1,050K | 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 | Openai (Standard), Openrouter (Standard) | Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, computer-use, reasoning, tools, vision |
GPT-5.6 Luna Capabilities
GLM 5V Turbo Capabilities
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Primary Evidence
Sources and Freshness
Questions
GPT-5.6 Luna vs GLM 5V Turbo FAQs
Is GPT-5.6 Luna or GLM 5V Turbo better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.6 Luna and GLM 5V Turbo, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-5.6 Luna or GLM 5V Turbo?+
Only GPT-5.6 Luna has a directly sourced input price: $0.20 per million tokens. Only GPT-5.6 Luna has a directly sourced output price: $1.20 per million tokens.
Which has a larger context window, GPT-5.6 Luna or GLM 5V Turbo?+
GPT-5.6 Luna has the larger sourced context window. GPT-5.6 Luna supports 1,050K and GLM 5V Turbo supports 200K.
Which performs better in benchmarks, GPT-5.6 Luna 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 GPT-5.6 Luna or GLM 5V Turbo be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-5.6 Luna is not marked open weight; GLM 5V Turbo is not marked open weight.
Can GPT-5.6 Luna and GLM 5V Turbo understand images?+
GPT-5.6 Luna is 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, GPT-5.6 Luna or GLM 5V Turbo?+
GLM 5V Turbo has the larger sourced maximum output: GPT-5.6 Luna supports 128K and GLM 5V Turbo supports 131K output tokens.
Do GPT-5.6 Luna and GLM 5V Turbo support reasoning and tool use?+
GPT-5.6 Luna: reasoning, tool calling, and image input. GLM 5V Turbo: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-5.6 Luna or GLM 5V Turbo?+
GPT-5.6 Luna has 2 sourced provider routes; GLM 5V Turbo has 1, so GPT-5.6 Luna has broader tracked availability.
Which offers better value, GPT-5.6 Luna 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.