Lyria 3.5 vs GPT-6 Astra
At a Glance
| Compare |  Lyria 3.5Google DeepMind |  GPT-6 AstraOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #6 of 4684.4 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #41 of 44$0.520 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #27 of 3849.2 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $10.00Openai ↗ · Sep 4, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $50.00Openai ↗ · Sep 4, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,050K |
| Model facts checked | Sep 22, 2026View model evidence → | Sep 4, 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 | Lyria 3.5 | GPT-6 Astra |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Lyria | Gpt 6 |
| Model | Lyria 3.5 | GPT-6 Astra |
| Version | Lyria 3.5 | GPT-6 Astra |
| Lifecycle | active | active |
| Released | 2026-07-29 | 2026-09-03 |
| Knowledge cutoff | Unknown | 2026-04-30 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text, Audio | Text |
| Context window | 1,049K | 1,050K |
| 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 | Google AI (Standard), Google Gemini (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | generation, image-to-music, lyrics, music-generation, synthid, vocals | chat, computer-use, generation, reasoning, research, tools |
Lyria 3.5 Capabilities
GPT-6 Astra Capabilities
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Primary Evidence
Sources and Freshness
Questions
Lyria 3.5 vs GPT-6 Astra FAQs
Is Lyria 3.5 or GPT-6 Astra better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Lyria 3.5 and GPT-6 Astra, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Lyria 3.5 or GPT-6 Astra?+
Only GPT-6 Astra has a directly sourced input price: $10.00 per million tokens. Only GPT-6 Astra has a directly sourced output price: $50.00 per million tokens.
Which has a larger context window, Lyria 3.5 or GPT-6 Astra?+
GPT-6 Astra has the larger sourced context window. Lyria 3.5 supports 1,049K and GPT-6 Astra supports 1,050K.
Which performs better in benchmarks, Lyria 3.5 or GPT-6 Astra?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Lyria 3.5 or GPT-6 Astra be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Lyria 3.5 is not marked open weight; GPT-6 Astra is not marked open weight.
Can Lyria 3.5 and GPT-6 Astra understand images?+
Lyria 3.5 is documented with image input; GPT-6 Astra is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Lyria 3.5 or GPT-6 Astra?+
GPT-6 Astra has the larger sourced maximum output: Lyria 3.5 supports 66K and GPT-6 Astra supports 128K output tokens.
Do Lyria 3.5 and GPT-6 Astra support reasoning and tool use?+
Lyria 3.5: image input. GPT-6 Astra: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Lyria 3.5 or GPT-6 Astra?+
Lyria 3.5 has 2 sourced provider routes; GPT-6 Astra has 2, a tie.
Which offers better value, Lyria 3.5 or GPT-6 Astra?+
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.