Model comparison
Compare GPT-6 Astra and Muse Spark 1.3 using the same provider-sourced text & reasoning rubric. No mystery score and no invented benchmark ranking.
Facts checked September 4, 2026
Set your usage. Your estimate updates as you type.
Example: 6,000 input tokens for 10 pages, plus a 500-token summary. Page lengths vary; adjust the numbers below.
One run sends your input to the model once and receives an answer. Tokens are pieces of text: input is what you send, output is the answer you receive.
Estimates exclude taxes, tools, cache storage/writes, free allowances and custom discounts. Image estimates cover output only, not prompt or reference-image charges. Quality modes differ by model. Unlisted settings are not treated as free.
| Model | Access | Estimated total (USD) |
|---|---|---|
| GPT-6 AstraOpenAI | OpenAI | $0.085 |
| Muse Spark 1.3Meta | Meta | No reviewed rate |
Quick take
OpenAI's most capable model for difficult end-to-end work, combining frontier reasoning with a million-token context window and a broad set of agent tools.
Access is still rolling out, fine-tuning is not supported, and requests above 272K input tokens use higher rates across the full request. Confirm access and measure total task cost before standardizing on Astra.
Meta's latest multimodal reasoning model for long-running agents, coding, tools, and complex user collaboration.
Meta's public launch does not publish every operational limit or one complete API data-use commitment. Check the live model page, price, region, retention controls, and maximum output before production.
Compare the published facts
Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.
| Text & reasoning | GPT-6 Astra | Muse Spark 1.3 |
|---|---|---|
| Context windowMaximum combined prompt and working context documented by the provider. | 1.05M tokens | 1M active context management in the Muse Spark API family |
| Maximum outputProvider-published response limit, where available. | 128K tokens | Not separately published |
| Knowledge cutoffLatest reliable knowledge date explicitly published by the model provider. Search and connected tools can retrieve newer information but do not change the model's built-in cutoff. | April 30, 2026 | Not published |
| Input priceCurrent standard list price per million input tokens unless noted. | $10 / 1M | See live Meta Model API price |
| Output priceCurrent standard list price per million output tokens unless noted. | $50 / 1M | See live Meta Model API price |
| InputsMedia types accepted by the listed model endpoint. | Text, image | Text, image, video, PDF |
| Tools & agentsSelected native tools and agent-building capabilities, not an exhaustive list. | Functions, search, code, computer, image, MCP | Parallel tools, coding, long-running agents, high reasoning |
How to choose
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
OpenAI says API content is not used for model training by default. Standard abuse-monitoring logs may be retained for up to 30 days, with additional controls available to qualifying organizations.
Provider and API links
The public Muse Spark model and release pages reviewed do not state one complete API retention or training-use commitment. Check the current Meta Model API terms and account controls before using private data.
Provider and API links
Frequently asked questions
GPT-6 Astra: OpenAI's most capable model for difficult end-to-end work, combining frontier reasoning with a million-token context window and a broad set of agent tools. Muse Spark 1.3: Meta's latest multimodal reasoning model for long-running agents, coding, tools, and complex user collaboration.
Consider GPT-6 Astra when your priority is Complex, long-running agent workflows. Consider Muse Spark 1.3 when your priority is Long-horizon coding and agent projects. Test both with your own data and provider route before committing.
No. This comparison aligns provider-published facts for the Text & reasoning category. It does not claim a universal winner or combine incompatible third-party benchmark scores.
