GPT-5 vs Muse Spark 1.1
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GPT-5 | Muse Spark 1.1 |
|---|---|---|
| Provider | OpenAI | Meta |
| Input Price (1M) | $1.25 | $1.25 |
| Output Price (1M) | $10.00 | $4.25 |
| Context Window | 400,000 | 1,048,576 |
Verdict
GPT-5 costs $1.25 per 1M input tokens and $10.00 per 1M output tokens. Muse Spark 1.1 costs $1.25 per 1M input tokens and $4.25 per 1M output tokens. GPT-5 and Muse Spark 1.1 have identical input token pricing. For output tokens, Muse Spark 1.1 is the more affordable option at $4.25/1M vs $10.00.
On context window, Muse Spark 1.1 supports 1,048,576 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GPT-5
- ✓ You are already integrated with OpenAI
When to choose Muse Spark 1.1
- ✓ Your workload is output-heavy — Muse Spark 1.1 generates text cheaper
- ✓ You need a larger context window (1,048,576 tokens)
- ✓ You are already integrated with Meta
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is GPT-5 cheaper than Muse Spark 1.1? ▼
GPT-5 and Muse Spark 1.1 have identical input token pricing at $1.25/1M tokens.
What is the context window of GPT-5 vs Muse Spark 1.1? ▼
GPT-5 has a 400,000-token context window. Muse Spark 1.1 has a 1,048,576-token context window. Muse Spark 1.1 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GPT-5 or Muse Spark 1.1? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GPT-5 is the cheaper option. For maximum context length, Muse Spark 1.1 supports 1,048,576 tokens. Use the comparison table above to find the right fit for your workload.