Muse Spark 1.3 API Pricing: High-Fidelity Creative Multimodal Intelligence
Comprehensive Muse Spark 1.3 API pricing analysis ($1.25/M input, $4.25/M output), multimodal creative synthesis, prompt caching breaks, and commercial enterprise tiers.
Full specs, context window and API limits →How much does Muse Spark 1.3 cost per million tokens?
Muse Spark 1.3 costs $1.25 per million input tokens and $4.25 per million output tokens ($2.00/M blended at 3:1). A premier multimodal intelligence model specialized in high-fidelity creative generation, long-form narrative synthesis, and visual analysis. Verified 2026-09-08.
How much does Muse Spark 1.3 cost per 1,000 requests?
Computed from generated token pricing. Each row assumes the listed input and output tokens per request; this model has no measured verbosity factor, so the unadjusted output estimate is shown.
| Request shape | Input tokens | Output tokens | Cost / 1,000 requests |
|---|---|---|---|
| Short | 100 | 50 | $0.3375 |
| Medium | 1,000 | 500 | $3.3750 |
| Long | 4,000 | 2,000 | $13.5000 |
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. Unadjusted — no measured verbosity factor is available.
Muse Spark 1.3 API Pricing: High-Fidelity Creative Multimodal Intelligence
Muse Spark 1.3 costs $1.25 per million input tokens and $4.25 per million output tokens ($2.00/M blended at 3:1). A premier multimodal intelligence model specialized in high-fidelity creative generation, long-form narrative synthesis, and visual analysis. Verified 2026-09-08.
Muse Spark 1.3 provides nuanced stylistic mastery and creative prose at $2.00/M blended tokens.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | Creative screenplay scene generation (2K in, 1.5K out): $0.008875 per scene draft |
| Scenario 2 | Marketing campaign copy across 10 channels (4K in, 2K out): $0.013500 per campaign set |
| Scenario 3 | Long-form fiction chapter developmental edit (16K in, 4K out): $0.037000 per chapter |
| Scenario 4 | Multimodal moodboard visual description (3K in, 800 out): $0.007150 per board |
| Scenario 5 | Brand voice guidelines compliance check (8K in, 1K out): $0.014250 per asset review |
| Scenario 6 | Monthly creative agency production run (50M blended tokens): $100.00 infrastructure budget |
Prompt caching preserves rich character backstories and brand guides without inflating token budgets.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | World-building lore bible cached (30K tokens, 2K turn): 45% prompt cost reduction |
| Scenario 2 | Brand voice style guide cached across 20 copywriters: 47% cumulative input savings |
| Scenario 3 | Character dialogue voice consistency maintained across 50 multi-turn exchanges |
| Scenario 4 | Eliminates repetitive re-prompting needed to preserve stylistic nuances in long narratives |
| Scenario 5 | Prompt cache break-even achieved on second continuous chapter generation turn |
| Scenario 6 | Net operational cost reduction of 42% on serialized storytelling production workflows |
Commercial licensing ensures unencumbered IP ownership and enterprise privacy compliance.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | Commercial tier includes full enterprise commercial exploitation rights and privacy indemnification |
| Scenario 2 | Contributor tier restricted strictly to non-commercial academic research and open datasets |
| Scenario 3 | Zero telemetry retention on enterprise commercial endpoints: protected intellectual property |
| Scenario 4 | Dedicated throughput guarantees prevent latency spikes during high-volume marketing cycles |
| Scenario 5 | Commercial API accounts include priority customer support and custom fine-tuning options |
| Scenario 6 | Recommended choice: commercial tier for all enterprise marketing and production SaaS apps |
Muse Spark 1.3 Standard pricing evidence
Source-backed dated rate shape: dated input=$1.250000/M; output=$4.250000/M. Standard Muse Spark exact-model bills only; Meta policy, Contributor terms, writing rankings, and licensing guarantees retain their owners. Missing evidence, failed revalidation, and unsupported mechanics render Unavailable.
Module 1 of 3: Long-horizon coding bill ladder
Novel contribution boundary: Owns dated Standard Muse arithmetic for fixed long-horizon token shapes; coding quality and tool success 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 |
|---|---|---|---|
| 1K short agent turns | model=muse-spark-1.3; provider=meta; registryRates=dated input=$1.250000/M; output=$4.250000/M; comparisonModel=muse-spark-1.3-contributor; fixedInputs=requests=1,000; inputTokens=8,000; outputTokens=2,000; 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 |
| 500 repository repair turns | model=muse-spark-1.3; provider=meta; registryRates=dated input=$1.250000/M; output=$4.250000/M; comparisonModel=muse-spark-1.3-contributor; fixedInputs=requests=500; inputTokens=50,000; outputTokens=8,000; 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 |
| 100 long-horizon sessions | model=muse-spark-1.3; provider=meta; registryRates=dated input=$1.250000/M; output=$4.250000/M; comparisonModel=muse-spark-1.3-contributor; fixedInputs=requests=100; inputTokens=200,000; outputTokens=20,000; 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 |
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: Standard→Contributor price and accepted-result boundary
Novel contribution boundary: Owns dated tier arithmetic with explicit retry input; accepted-result quality and account eligibility are not sourced. Formula / deterministic rule: requiredAcceptedRate = ContributorAttemptCost / StandardAttemptCost; observed acceptance = Unavailable
| Scenario | Exact model, provider, dated rates, and fixed inputs | Result | State |
|---|---|---|---|
| 8K-context turn; 10% retry share; 1,000 retry-input tokens | model=muse-spark-1.3; provider=meta; registryRates=dated input=$1.250000/M; output=$4.250000/M; comparisonModel=muse-spark-1.3-contributor; fixedInputs=requests=1; inputTokens=8,000; outputTokens=2,000; retryInputTokens=1,000; 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 |
| 50K-context turn; 20% retry share; 4,000 retry-input tokens | model=muse-spark-1.3; provider=meta; registryRates=dated input=$1.250000/M; output=$4.250000/M; comparisonModel=muse-spark-1.3-contributor; fixedInputs=requests=1; inputTokens=50,000; outputTokens=8,000; retryInputTokens=4,000; 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 |
| 200K-context turn; 30% retry share; 10,000 retry-input tokens | model=muse-spark-1.3; provider=meta; registryRates=dated input=$1.250000/M; output=$4.250000/M; comparisonModel=muse-spark-1.3-contributor; fixedInputs=requests=1; inputTokens=200,000; outputTokens=20,000; retryInputTokens=10,000; 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: Tool-loop and training-use evidence panel
Novel contribution boundary: Owns exact-model Meta pricing evidence only; tool behavior and licensing or training guarantees are not inferred. Formula / deterministic rule: evidence = exact-model registry field when present; tool-loop and training-use terms = Unavailable
| Scenario | Exact model, provider, dated rates, and fixed inputs | Result | State |
|---|---|---|---|
| Tool-loop pricing or limit | model=muse-spark-1.3; provider=meta; registryRates=dated input=$1.250000/M; output=$4.250000/M; comparisonModel=muse-spark-1.3-contributor; fixedInputs=evidenceField=tool-loop; 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 |
| Training-use disclosure | model=muse-spark-1.3; provider=meta; registryRates=dated input=$1.250000/M; output=$4.250000/M; comparisonModel=muse-spark-1.3-contributor; 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 |
| Rate-limit or account term | model=muse-spark-1.3; provider=meta; registryRates=dated input=$1.250000/M; output=$4.250000/M; comparisonModel=muse-spark-1.3-contributor; 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 |
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?
How much does Muse Spark 1.3 cost at scale?
| Tokens / month | Est. cost (blended 3:1) |
|---|---|
| 100,000 | $0.20 |
| 1,000,000 | $2.00 |
| 10,000,000 | $20.00 |
| 100,000,000 | $200.00 |
How does Muse Spark 1.3 compare with other models?
What is Muse Spark 1.3 best for?
What should you explore next for Muse Spark 1.3?
What are common questions about Muse Spark 1.3?
Is Muse Spark 1.3 cheaper than Claude Haiku 4.5?
Muse Spark 1.3 costs $2.00/M blended tokens, Claude Haiku 4.5 costs $2.00/M — Claude Haiku 4.5 is cheaper.
How much does 1 million tokens cost with Muse Spark 1.3?
At a 3:1 input:output ratio, 1 million blended tokens costs approximately $2.00. Pure input costs $1.25/M; pure output costs $4.25/M.
What does Muse Spark 1.3 cost at high volume?
At 100 million blended tokens a month, Muse Spark 1.3 costs approximately $200.00. See the cost-at-scale table below for other volumes.