LLM Comparison
Apodex 1.1 vs Muse Spark 1.1
Side-by-side specs, pricing & capabilities
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 47 16.9 | |
| Family | Apodex | Muse Spark |
| Status | Current | Current |
| Release Date | Aug 2026 | Jul 2026 |
| Context Window | 262K tokens | 1.0M tokens |
| Input Price | $0.24/M tokens | $1.25/M tokens |
| Output Price | $2.40/M tokens | $4.25/M tokens |
| Pricing Notes | Limited-time 20% discount, USD per 1M tokens: $0.24 input, $0.024 cached input and $2.40 output. Standard prices are $0.30, $0.03 and $3.00. Requests above 200K input tokens cost 2x all applicable rates for the whole request. No campaign end date is stated; recheck pricing before use. | Artificial Analysis reports Meta API pricing at $1.25 input, $4.25 output, and $0.15 cached input per 1M tokens for the xhigh variant. |
| Capabilities | textreasoningcodelong-contextstreaming | reasoningtool-usecomputer-usecodingmultimodallong-contextstructured-output |
| Max Output | 262K tokens | 131K tokens |
| API Identifier | apodex-1.1 | muse-spark-1.1 |
| Benchmarks | ||
| Artificial Analysis Intelligence Index v4.3 | — | 34.3 indexartificial-analysis |
| Artificial Analysis Intelligence Index v4.3 | — | 34 indexhttps://artificialanalysis.ai/models/muse-spark-1-1/ |
| GPQA Diamond | 86.4%artificial-analysis | 89.8%artificial-analysis |
| SciCode | 45.5%artificial-analysis | 58.8%artificial-analysis |
| Terminal-Bench 2.1 | 69.7%artificial-analysis | 77.9%artificial-analysis |
| View Apodex 1.1 | View Muse Spark 1.1 | |
Cost Calculator
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| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| Apodex 1.1Cheapest | $0.24 | $1.20 | $1.44 | — |
| Muse Spark 1.1 | $1.25 | $2.13 | $3.38 | +134% |
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Apodex
Apodex 1.1
Apodex 1.1 is a proprietary long-context reasoning model for complex professional work, research, coding, and evidence-heavy analysis through a first-party API.
Meta
Muse Spark 1.1
Muse Spark 1.1 is Meta's multimodal reasoning model for agentic coding, computer-use workflows, tool calling, and long-context tasks through the Meta Model API.
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