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Model comparison · accepted catalogue facts 2026-10-21

Muse Spark 1.1 vs Muse Spark 1.2

Muse Spark 1.2 has an equal-or-higher measured score and an equal-or-lower estimated price. No known model-capability loss from Muse Spark 1.1; endpoint and task checks still apply. The reported score intervals overlap; this is not an established quality improvement.

Editorial comparison: Meta tier choice: Muse Spark 1.2 vs Muse Spark 1.1 with comparable measured benchmark scores Inclusion does not recommend either model.

Muse Spark 1.1

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1478.1

Balanced effective USD / 1M tokens · lower is better

$2.00/M

Muse Spark 1.2

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1484.9

Balanced effective USD / 1M tokens · lower is better

$2.00/M

In-Depth Analysis

Muse Spark 1.1 vs Muse Spark 1.2: Measured Technical Breakdown

Comprehensive comparison across measured human-preference evaluation, effective production token costs, architectural limits, and migration trade-offs.

Measured Capability (LMArena)
Muse Spark 1.2 leads by 6.8 Elo

Muse Spark 1.1: 1478.1 ±4.25 · Muse Spark 1.2: 1484.9 ±7.38 Intervals overlap — not a guaranteed quality advantage

Cost Comparison Across Canonical Production Workloads

Rates represent effective cost per 1M tokens under standard token blends. Cached prompt reads are factored according to workload context reuse.

Workload & RatioMuse Spark 1.1Muse Spark 1.2Cost DifferenceAdvantage
Balanced 3 tokens in per 1 out$2.00 /1M tokens$2.00 /1M tokens$0.0000 Same price
Summarise Huge input, tiny output$1.09 /1M tokens$1.09 /1M tokens$0.0000 Same price
Chat Long cached system prompt$2.12 /1M tokens$2.12 /1M tokens$0.0000 Same price
Code gen Output dominates$2.96 /1M tokens$2.96 /1M tokens$0.0000 Same price
Agentic Context replayed each turn$1.05 /1M tokens$1.05 /1M tokens$0.0000 Same price

Architectural & Operational Specifications

SpecificationMuse Spark 1.1Muse Spark 1.2Operational Note
Context window1,048,576 tokens1,048,576 tokens—
Max output generation943,718 tokens943,718 tokens—
Base input price$1.25 / 1M tokens$1.25 / 1M tokens—
Base output price$4.25 / 1M tokens$4.25 / 1M tokens—
Cached input price$0.150 / 1M tokens$0.150 / 1M tokens—
Reasoning & thinking tokensSupported; standard ratesSupported; standard rates—
Input modalitiestext, image, video, file, audiotext, image, video, file, audio—
Tool calling / function useSupportedSupported—
Open weights & licenceProprietary (API-only)Proprietary (proprietary)Muse Spark 1.2 allows independent hosting or local deployment
Provider & reseller marketMeta · 1 provider offersMeta · 1 provider offers—

When to Choose Muse Spark 1.1

  • Direct integration with Meta tooling and API features.

When to Choose Muse Spark 1.2

  • Higher measured general capability (1484.9 Elo on human evaluation).
  • Direct integration with Meta tooling and API features.

Detailed Evidence Passports & Independent Audits

Muse Spark 1.1 Full model card, pricing ladder & provider offers → Muse Spark 1.2 Full model card, pricing ladder & provider offers → Alternatives to Muse Spark 1.1 Better-and-cheaper dominance audit → Alternatives to Muse Spark 1.2 Better-and-cheaper dominance audit →

Compare · model decision

Choose two or three models

Inspect the differences, estimate your workload and keep the evidence. All standard catalogue models are available, including unrated ones.

Catalogue facts
2026-10-21

2 / 3 selected · 0 requirements
Open public comparison link
Public workload and requirements

These settings are included in the share link. The fixed-request scenario and private evaluation data below are excluded.

A ↔ B

Muse Spark 1.2 has an equal-or-higher measured score and an equal-or-lower estimated price. No known model-capability loss from Muse Spark 1.1; endpoint and task checks still apply. The reported score intervals overlap; this is not an established quality improvement.

Estimated blend: 75% input, 25% output; 0% cached input. Base context tier. Prompt length selects the billing tier. An unlisted cache-read rate uses that tier’s full input rate in this blend; missing required input or output rates prevent an estimate. The fixed-request estimate below validates actual context and output limits and requires a published cache-read rate when cache hits are assumed.

What changes between these models

Quality first, then estimated price. LMArena scores are human-preference measurements, not a benchmark of your application. A missing score is unrated.

  • Muse Spark 1.1 · Meets the stated model requirements
  • Muse Spark 1.2 · Meets the stated model requirements
Material differences and unknown evidence. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA Muse Spark 1.1B Muse Spark 1.2
LMArena general score (Elo)1,478.11,484.9Δ vs A: +6.8 Elo
Score interval half-width (± Elo)4.257.38Δ vs A: +3.13 Elo
Benchmark reasoning effortdefaultxhigh
LMArena document score (Elo)1,464.7Unknown
Open weightsUnknownNot supported
Recorded licenceUnknownproprietary
Recorded retirement dateUnknownUnknown
13 shared measured facts
Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA Muse Spark 1.1B Muse Spark 1.2
Estimated effective USD / 1M tokens$2$2Δ vs A: 0 USD/M
Base input USD / 1M tokens$1.25$1.25Δ vs A: 0 USD/M
Base output USD / 1M tokens$4.25$4.25Δ vs A: 0 USD/M
Base cached-input USD / 1M tokens$0.15$0.15Δ vs A: 0 USD/M
Context window (tokens)1,048,5761,048,576Δ vs A: 0 tokens
Output limit (tokens)943,718943,718Δ vs A: 0 tokens
Input modalitiestext, image, video, file, audiotext, image, video, file, audio
Tool useSupportedSupportedΔ vs A: 0
ReasoningSupportedSupportedΔ vs A: 0
Model providerMetaMeta
Distinct sellers in the catalogue11Δ vs A: 0
Serving precision differs across offersNot supportedNot supportedΔ vs A: 0
Retirement evidenceNone announced; not a guaranteeNone announced; not a guarantee

Provider counts and precision flags describe catalogue offers. They do not establish the precision, availability, region, tool behaviour or limits of your chosen endpoint. Open each model’s provider table before switching.

Complete price ladders and billing conditions

Muse Spark 1.1

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token thresholdInputOutputCached input
Base, up to and including first threshold$1.25$4.25$0.15

Muse Spark 1.2

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token thresholdInputOutputCached input
Base, up to and including first threshold$1.25$4.25$0.15
Migration preflight · compare each candidate with model A

Keeping Muse Spark 1.1 is a valid decision. No pairwise comparison certifies a drop-in replacement.

Muse Spark 1.1 → Muse Spark 1.2

No known model-capability loss

  • Provider endpoint, serving precision, availability and task success need your own verification. Catalogue model capabilities do not certify an endpoint.
Check alternatives to Muse Spark 1.1

Estimate a fixed request shape

Private to this tab unless you save locally or explicitly export private details. These are estimates, not observed invoices. Cross-model token counts and task success are assumptions.

0.1 extra attempts means 10 extra full attempts per 100 requests. Cache writes, tool charges, taxes and unlisted charges are excluded.

  • Muse Spark 1.1Estimated total: $4.625 1,000 full attempts · base rates
  • Muse Spark 1.2Estimated total: $4.625 1,000 full attempts · base rates
Prompt and output sensitivity · sampled scenarios

Every point recalculates the entire request shape. Tier-boundary samples include one token below, at and above each threshold. Ordering between samples is not guaranteed. Lowest known cost excludes unknown estimates and is not a replacement recommendation.

Vary input; hold output, requests, cache and retries fixed.
Input tokensMuse Spark 1.1Muse Spark 1.2Coverage
2,000$4.625Lowest known$4.625Lowest knownAll computed
4,096$7.245Lowest known$7.245Lowest knownAll computed
32,768$43.085Lowest known$43.085Lowest knownAll computed
131,072$165.965Lowest known$165.965Lowest knownAll computed
Vary output; hold input, requests, cache and retries fixed.
Output tokensMuse Spark 1.1Muse Spark 1.2
128$3.044$3.044
2,048$11.204$11.204
8,192$37.316$37.316

Bring your observed evaluation results

JSON is parsed locally. Prompts, filenames, individual responses and extra fields are not retained. Import only aggregate measurements for selected models. Task/version labels do not establish that two evaluations used the same dataset.

Expected format
[{"model":"provider/exact-model-id","task":"support-answer","version":"rubric-1","attempts":100,"successes":84,"costUsd":2.4,"latencyMs":950}]

Use the exact catalogue ID. Replace the example values with observed measurements. Omit costUsd or latencyMs when they are unknown. No public score is inferred from these observations.

Keep the decision and its evidence

A portable record retains selected facts, complete tier ladders, public requirements and the publication manifest. It also embeds the full permitted public catalogue to verify the input artifact checksum. Files are larger for this reason (up to 2 MB); local storage is limited to 12 records. Team review is local to this browser; there is no hosted collaboration.

Saving is opt-in and includes private review fields. Shared comparison links never include them. Version 1 Check references remain separate and are not overwritten or guessed into this format.

Saved local decisions · 0

No version 2 decision records are stored in this browser.

Questions this page answers

Is Muse Spark 1.1 a better-and-cheaper replacement for Muse Spark 1.2?

Muse Spark 1.2 has an equal-or-higher measured score and an equal-or-lower estimated price. No known model-capability loss from Muse Spark 1.1; endpoint and task checks still apply. The reported score intervals overlap; this is not an established quality improvement.

Which model is higher rated: Muse Spark 1.1 or Muse Spark 1.2?

Muse Spark 1.2 scores higher on LMArena general (1484.9 ±7.4) compared to Muse Spark 1.1 (1478.1 ±4.3), a margin of 6.8 Elo points. However, because the 95% confidence intervals overlap, this does not establish an unconditional superiority across all tasks.

Which model is more cost-effective: Muse Spark 1.1 or Muse Spark 1.2?

Both Muse Spark 1.1 and Muse Spark 1.2 share an identical effective price of $2.00 per 1M tokens on a balanced workload.

How do their context windows and generation limits compare?

Both Muse Spark 1.1 and Muse Spark 1.2 provide an identical 1,048,576-token context window. Maximum output generation limits are 943,718 tokens for Muse Spark 1.1 and 943,718 tokens for Muse Spark 1.2.

What capabilities are lost when switching between Muse Spark 1.1 and Muse Spark 1.2?

Neither model exhibits a confirmed loss in declared context or modalities when switching between them. However, differences in tokenizer efficiency, prompt alignment, and latency across providers should be benchmarked on task-specific test suites.

Can Muse Spark 1.1 or Muse Spark 1.2 be self-hosted or deployed privately?

Both Muse Spark 1.1 and Muse Spark 1.2 are proprietary cloud models offered via managed API endpoints. Muse Spark 1.1 is served by Meta and 1 catalogue offers, while Muse Spark 1.2 is served by Meta and 1 catalogue offers.

What is included in this comparison?

Accepted model capabilities, context and output limits, complete price-tier ladders, independent LMArena measurements and missing evidence. Provider endpoint precision and application success still need verification.

Can I compare a different workload or keep a decision record?

Yes. Change the public workload, prompt length and requirements in the comparison workspace. Fixed-request scenarios, aggregate evaluations and review notes stay local unless explicitly exported.

Evidence & Ask
Markdown for LLMs