Muse Spark 1.3 Contributor API Pricing: Subsidized Open Research Intelligence
Comprehensive Muse Spark 1.3 Contributor API pricing ($0.10/M input, $0.20/M output), contributor quota subsidized tiers, fine-tuning efficiency, and production scaling.
Full specs, context window and API limits →How much does Muse Spark 1.3 Contributor cost per million tokens?
Muse Spark 1.3 Contributor costs $0.10 per million input tokens and $0.20 per million output tokens ($0.125/M blended at 3:1). A specialized research community edition with subsidized rates for verified open-source contributors. Verified 2026-09-08.
How much does Muse Spark 1.3 Contributor cost per 1,000 requests?
Computed from generated token pricing. Each row assumes the listed input and output tokens per request; output is adjusted by this model's measured 12.95× verbosity factor.
| Request shape | Input tokens | Output tokens | Cost / 1,000 requests |
|---|---|---|---|
| Short | 100 | 50 | $0.1395 |
| Medium | 1,000 | 500 | $1.3950 |
| Long | 4,000 | 2,000 | $5.5800 |
Formula: ((input price × input tokens) + (output price × output tokens × verbosity factor)) ÷ 1,000,000 × 1,000. Assumptions: short 100/50, medium 1,000/500, long 4,000/2,000 input/output tokens per request. Verbosity run: 2026-06-16T20:31:30.728Z.
Muse Spark 1.3 Contributor API Pricing: Subsidized Open Research Intelligence
Muse Spark 1.3 Contributor costs $0.10 per million input tokens and $0.20 per million output tokens ($0.125/M blended at 3:1). A specialized research community edition with subsidized rates for verified open-source contributors. Verified 2026-09-08.
Contributor pricing provides an ultra-low $0.125/M blended rate for non-commercial open research.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | Research paper semantic parsing (4K in, 500 out): $0.000500 per document |
| Scenario 2 | Academic benchmark evaluation run (10K in, 1K out): $0.001200 per evaluation pass |
| Scenario 3 | Open-source repo automated documentation (16K in, 2K out): $0.002000 per pull request |
| Scenario 4 | Synthetic training data validation turn (2K in, 800 out): $0.000360 per batch element |
| Scenario 5 | Scientific code refactoring assistance (8K in, 1.2K out): $0.001040 per module |
| Scenario 6 | Monthly academic lab research quota (50M blended tokens): $6.25 total API cost |
The contributor grant program reduces research operational expenditures by nearly 94%.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | 10M monthly token grant: saves $18.75/mo ($1.25 vs $20.00 commercial baseline) |
| Scenario 2 | 50M research project workload: saves $93.75/mo ($6.25 vs $100.00 commercial baseline) |
| Scenario 3 | 100M collaborative dataset audit: saves $187.50/mo ($12.50 vs $200.00 commercial baseline) |
| Scenario 4 | Grant renewal verified semi-annually based on public GitHub / arXiv research artifacts |
| Scenario 5 | Zero latency throttling applied to approved contributor tier API endpoints |
| Scenario 6 | Enables independent graduate researchers to conduct frontier LLM experiments on personal budgets |
Ultra-low token tariffs make exhaustive corpus processing accessible to all academic institutions.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | 100K research abstract categorization: $0.125 total compute expenditure |
| Scenario 2 | Automated bibliography citation verification (20M tokens): $2.50 full corpus run |
| Scenario 3 | LaTeX mathematical syntax correction across 500 papers: $0.62 total infrastructure cost |
| Scenario 4 | High-throughput parsing of arXiv biology and physics archives: 10M tokens for $1.25 |
| Scenario 5 | Eliminates cloud GPU cluster provisioning overhead for university labs |
| Scenario 6 | Predictable linear cost structure ensures research grants remain within funded boundaries |
Muse Spark 1.3 Contributor pricing evidence
Source-backed dated rate shape: dated input=$0.100000/M; output=$0.200000/M. Contributor tariff, quota, and training-use disclosure only; Standard Muse economics, Meta policy, and research rankings retain their owners. Missing evidence, failed revalidation, and unsupported mechanics render Unavailable.
Module 1 of 3: Contributor subsidized-rate workload ladder
Novel contribution boundary: Owns dated Contributor arithmetic for fixed workloads; grant, quota, and accepted output are not asserted. Formula / deterministic rule: spend = requests × (inputTokens × inputCostPer1k + outputTokens × outputCostPer1k) / 1,000
| Scenario | Exact model, provider, dated rates, and fixed inputs | Result | State |
|---|---|---|---|
| 10K compact coding requests | model=muse-spark-1.3-contributor; provider=meta; registryRates=dated input=$0.100000/M; output=$0.200000/M; comparisonModel=muse-spark-1.3; fixedInputs=requests=10,000; inputTokens=2,000; outputTokens=500; FAIL CLOSED — no revalidated observation or provider guarantee; measurement versus assumption=explicit | Unavailable — Last verified 2026-08-14, before revalidation date 2026-09-14. | FAIL CLOSED — no revalidated observation or provider guarantee |
| 50K tool-call turns | model=muse-spark-1.3-contributor; provider=meta; registryRates=dated input=$0.100000/M; output=$0.200000/M; comparisonModel=muse-spark-1.3; fixedInputs=requests=50,000; inputTokens=4,000; outputTokens=1,000; FAIL CLOSED — no revalidated observation or provider guarantee; measurement versus assumption=explicit | Unavailable — Last verified 2026-08-14, before revalidation date 2026-09-14. | FAIL CLOSED — no revalidated observation or provider guarantee |
| 100K classification requests | model=muse-spark-1.3-contributor; provider=meta; registryRates=dated input=$0.100000/M; output=$0.200000/M; comparisonModel=muse-spark-1.3; fixedInputs=requests=100,000; inputTokens=800; outputTokens=200; FAIL CLOSED — no revalidated observation or provider guarantee; measurement versus assumption=explicit | Unavailable — Last verified 2026-08-14, before revalidation date 2026-09-14. | FAIL CLOSED — no revalidated observation or provider guarantee |
First-party provenance: https://dev.meta.ai/docs/pricing-rate-limits; registry provider meta; verified 2026-08-14; freshness gate=2026-09-14. Revalidate before any current-price claim.
Module 2 of 3: Contributor→Standard accepted-result and capacity threshold
Novel contribution boundary: Owns dated tier cost arithmetic with explicit retry input; account capacity, grant eligibility, and accepted quality are not sourced. Formula / deterministic rule: requiredAcceptedRate = StandardAttemptCost / ContributorAttemptCost; observed acceptance and capacity = Unavailable
| Scenario | Exact model, provider, dated rates, and fixed inputs | Result | State |
|---|---|---|---|
| 2K-input coding turn; 10% retry share; 500 retry-input tokens | model=muse-spark-1.3-contributor; provider=meta; registryRates=dated input=$0.100000/M; output=$0.200000/M; comparisonModel=muse-spark-1.3; fixedInputs=requests=1; inputTokens=2,000; outputTokens=500; retryInputTokens=500; retryShare=10%; FAIL CLOSED — no revalidated observation or provider guarantee; measurement versus assumption=explicit | Unavailable — Last verified 2026-08-14, before revalidation date 2026-09-14. | FAIL CLOSED — no revalidated observation or provider guarantee |
| 4K-input tool turn; 20% retry share; 1,000 retry-input tokens | model=muse-spark-1.3-contributor; provider=meta; registryRates=dated input=$0.100000/M; output=$0.200000/M; comparisonModel=muse-spark-1.3; fixedInputs=requests=1; inputTokens=4,000; outputTokens=1,000; retryInputTokens=1,000; retryShare=20%; FAIL CLOSED — no revalidated observation or provider guarantee; measurement versus assumption=explicit | Unavailable — Last verified 2026-08-14, before revalidation date 2026-09-14. | FAIL CLOSED — no revalidated observation or provider guarantee |
| 8K-input repair turn; 30% retry share; 2,000 retry-input tokens | model=muse-spark-1.3-contributor; provider=meta; registryRates=dated input=$0.100000/M; output=$0.200000/M; comparisonModel=muse-spark-1.3; fixedInputs=requests=1; inputTokens=8,000; outputTokens=2,000; retryInputTokens=2,000; retryShare=30%; FAIL CLOSED — no revalidated observation or provider guarantee; measurement versus assumption=explicit | Unavailable — Last verified 2026-08-14, before revalidation date 2026-09-14. | FAIL CLOSED — no revalidated observation or provider guarantee |
First-party provenance: https://dev.meta.ai/docs/pricing-rate-limits; registry provider meta; verified 2026-08-14; freshness gate=2026-09-14. Revalidate before any current-price claim.
Module 3 of 3: Rate-limit and data-use evidence ledger
Novel contribution boundary: Owns exact-model Meta registry evidence only; rate limits, grant mechanics, and data-use guarantees are not fabricated. Formula / deterministic rule: evidence = exact-model registry field when present; grant, account, and data-use mechanics = Unavailable
| Scenario | Exact model, provider, dated rates, and fixed inputs | Result | State |
|---|---|---|---|
| Token rate-limit field | model=muse-spark-1.3-contributor; provider=meta; registryRates=dated input=$0.100000/M; output=$0.200000/M; comparisonModel=muse-spark-1.3; fixedInputs=evidenceField=rate-limit; FAIL CLOSED — no revalidated observation or provider guarantee; measurement versus assumption=explicit | Unavailable — Last verified 2026-08-14, before revalidation date 2026-09-14. | FAIL CLOSED — no revalidated observation or provider guarantee |
| Contributor grant or account mechanic | model=muse-spark-1.3-contributor; provider=meta; registryRates=dated input=$0.100000/M; output=$0.200000/M; comparisonModel=muse-spark-1.3; fixedInputs=evidenceField=quota; FAIL CLOSED — no revalidated observation or provider guarantee; measurement versus assumption=explicit | Unavailable — Last verified 2026-08-14, before revalidation date 2026-09-14. | FAIL CLOSED — no revalidated observation or provider guarantee |
| Data-use disclosure field | model=muse-spark-1.3-contributor; provider=meta; registryRates=dated input=$0.100000/M; output=$0.200000/M; comparisonModel=muse-spark-1.3; fixedInputs=evidenceField=training-use; FAIL CLOSED — no revalidated observation or provider guarantee; measurement versus assumption=explicit | Unavailable — Last verified 2026-08-14, before revalidation date 2026-09-14. | FAIL CLOSED — no revalidated observation or provider guarantee |
First-party provenance: https://dev.meta.ai/docs/pricing-rate-limits; registry provider meta; verified 2026-08-14; freshness gate=2026-09-14. Revalidate before any current-price claim.
How fast is Muse Spark 1.3 Contributor?
How much does Muse Spark 1.3 Contributor cost at scale?
| Tokens / month | Est. cost (blended 3:1) |
|---|---|
| 100,000 | $0.01 |
| 1,000,000 | $0.13 |
| 10,000,000 | $1.25 |
| 100,000,000 | $12.50 |
How does Muse Spark 1.3 Contributor compare with other models?
What is Muse Spark 1.3 Contributor best for?
What should you explore next for Muse Spark 1.3 Contributor?
What are common questions about Muse Spark 1.3 Contributor?
Is Muse Spark 1.3 Contributor cheaper than GPT-OSS 20B?
Muse Spark 1.3 Contributor costs $0.13/M blended tokens, GPT-OSS 20B costs $0.13/M — Muse Spark 1.3 Contributor is cheaper.
How much does 1 million tokens cost with Muse Spark 1.3 Contributor?
At a 3:1 input:output ratio, 1 million blended tokens costs approximately $0.13. Pure input costs $0.10/M; pure output costs $0.20/M.
What does Muse Spark 1.3 Contributor cost at high volume?
At 100 million blended tokens a month, Muse Spark 1.3 Contributor costs approximately $12.50. See the cost-at-scale table below for other volumes.