Muse Spark 1.2 Contributor
meta/muse-spark-1-2-contributorVon Meta · Familie: muse · veröffentlicht 2026-08-05
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Fähigkeiten
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding93
- Tool calling40/40
- Structured output20/20
- Reasoning10/10
- Context window (100K → 1M)20/20
- Provider availability3/10
Agents93
- Tool calling35/35
- Structured output25/25
- Reasoning15/15
- Output token limit15/15
- Provider availability3/10
JSON / structured output99
- Structured output / JSON mode50/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency80
- Headline price (log-scaled)75/95
- Has prompt-cache pricing5/5
Long context91
- Context window (100K → 2M)81/90
- Has published price for full window10/10
Vision93
- Accepts image input50/50
- Context window (10K → 1M)30/30
- Has published price10/10
- Provider availability3/10
Production-readiness71
- Number of independent providers15/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers6/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.60 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $1.20 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.30 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $1.00 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $1.32 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Preis-Details
Empfohlene Preise von meta · muse-spark-1.2-contributor
Bei 3 Anbietern verfügbar
| Anbieter | Anbieter-Modell-ID | Eingabe / 1M | Ausgabe / 1M | Kontext | Veröffentlicht |
|---|---|---|---|---|---|
| Meta meta | muse-spark-1.2-contributor | $0.100 | $0.200 | 1.05M | 2026-08-05 |
| Vercel AI Gateway vercel | meta/muse-spark-1.2-contributor | $0.100 | $0.200 | 1.05M | 2026-08-05 |
| NanoGPT nano-gpt | meta/muse-spark-1.2-contributor | $0.100 | $0.200 | 1M | 2026-08-05 |
Datenunterschiede zwischen Anbietern
- context_window varies: 1000000, 1048576
- modalities varies across offerings
Anbieter melden unterschiedliche Werte für dieses Modell. Die Schnellinfos oben nutzen den repräsentativen Anbieter; pro Anbieter siehe Tabelle.
Frequently asked questions
How much does Muse Spark 1.2 Contributor cost?
Muse Spark 1.2 Contributor costs $0.100 per 1M input tokens and $0.200 per 1M output tokens, sourced from meta. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Muse Spark 1.2 Contributor?
Muse Spark 1.2 Contributor has a context window of 1.05M tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does Muse Spark 1.2 Contributor support tool calling?
Yes. Muse Spark 1.2 Contributor supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Muse Spark 1.2 Contributor support structured output / JSON mode?
Yes. Muse Spark 1.2 Contributor supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can Muse Spark 1.2 Contributor accept image input?
Yes. Muse Spark 1.2 Contributor accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check Meta's docs for the exact rule.
Is Muse Spark 1.2 Contributor open-weight?
No. Muse Spark 1.2 Contributor is a proprietary model — only Meta (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Muse Spark 1.2 Contributor?
If Muse Spark 1.2 Contributor doesn't fit, consider Llama-3.3-70B-Instruct, Llama 3.1 8B Instruct, Llama 4 Maverick 17B 128E Instruct FP8. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More Meta models
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- Llama 3.1 8B Instruct$0.02 in / $0.03 out
- Llama 4 Maverick 17B 128E Instruct FP8$0.14 in / $0.59 out
- Llama 3.2 3B Instruct$0.02 in / $0.02 out
- Llama 4 Scout 17B 16E Instruct$0.10 in / $0.30 out
Capability lists this model is in
Zuletzt aktualisiert:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.