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Model comparison · accepted catalogue facts 2026-10-21

Muse Spark 1.2 vs GPT-5.6 Sol

Muse Spark 1.2 has an equal-or-higher measured score and an equal-or-lower estimated price. Switching from GPT-5.6 Sol loses capability or fails a requirement. Review the migration checks. The reported score intervals do not overlap; this is not a task-specific success guarantee.

Editorial comparison: Cross-provider head-to-head: Muse Spark 1.2 (Meta) vs GPT-5.6 Sol (OpenAI) on intelligence, speed, and pricing Inclusion does not recommend either model.

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

GPT-5.6 Sol

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1457.2

Balanced effective USD / 1M tokens · lower is better

$8.00/M

In-Depth Analysis

Muse Spark 1.2 vs GPT-5.6 Sol: 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 27.7 Elo

Muse Spark 1.2: 1484.9 ±7.38 · GPT-5.6 Sol: 1457.2 ±4.34 Statistically significant preference separation

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 1.2GPT-5.6 SolCost DifferenceAdvantage
Balanced 3 tokens in per 1 out$2.00 /1M tokens$8.00 /1M tokens$6.00 (75% diff)Muse Spark 1.2 costs less
Summarise Huge input, tiny output$1.09 /1M tokens$3.77 /1M tokens$2.69 (71% diff)Muse Spark 1.2 costs less
Chat Long cached system prompt$2.12 /1M tokens$9.32 /1M tokens$7.20 (77% diff)Muse Spark 1.2 costs less
Code gen Output dominates$2.96 /1M tokens$13.31 /1M tokens$10.35 (78% diff)Muse Spark 1.2 costs less
Agentic Context replayed each turn$1.05 /1M tokens$4.26 /1M tokens$3.21 (75% diff)Muse Spark 1.2 costs less

Architectural & Operational Specifications

SpecificationMuse Spark 1.2GPT-5.6 SolOperational Note
Context window1,048,576 tokens1,050,000 tokens—
Max output generation943,718 tokens128,000 tokensMuse Spark 1.2 allows longer single-turn completions
Base input price$1.25 / 1M tokens$4.00 / 1M tokensMuse Spark 1.2 input rate is cheaper
Base output price$4.25 / 1M tokens$20.00 / 1M tokensMuse Spark 1.2 output rate is cheaper
Cached input price$0.150 / 1M tokens$0.400 / 1M tokensMuse Spark 1.2 offers lower cached prompt cost
Reasoning & thinking tokensSupported; standard ratesSupported; standard rates—
Input modalitiestext, image, video, file, audiofile, image, text—
Tool calling / function useSupportedSupported—
Open weights & licenceProprietary (proprietary)Proprietary (proprietary)—
Provider & reseller marketMeta · 1 provider offersOpenAI · 7 provider offers (2.2× spread)—

When to Choose Muse Spark 1.2

  • Higher measured general capability (1484.9 Elo on human evaluation).
  • Higher maximum completion limit (943,718 tokens).
  • Broader multimodal input support: text, image, video, file, audio.
  • Direct integration with Meta tooling and API features.
Risks when moving from GPT-5.6 Sol → Muse Spark 1.2:
  • Context window: 1,050,000 → 1,048,576 tokens.

When to Choose GPT-5.6 Sol

  • Larger context window (1,050,000 tokens) for long-document or repo-level context.
  • Direct integration with OpenAI tooling and API features.
Risks when moving from Muse Spark 1.2 → GPT-5.6 Sol:
  • Output limit: 943,718 → 128,000 tokens.
  • video input is not supported by the candidate.
  • audio input is not supported by the candidate.

Detailed Evidence Passports & Independent Audits

Muse Spark 1.2 Full model card, pricing ladder & provider offers → GPT-5.6 Sol Full model card, pricing ladder & provider offers → Alternatives to Muse Spark 1.2 Better-and-cheaper dominance audit → Alternatives to GPT-5.6 Sol 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. Switching from GPT-5.6 Sol loses capability or fails a requirement. Review the migration checks. The reported score intervals do not overlap; this is not a task-specific success guarantee.

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 1.2 · Meets the stated model requirements
  • GPT-5.6 Sol · 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 1.2B GPT-5.6 Sol
LMArena general score (Elo)1,484.91,457.2Δ vs A: -27.7 Elo
Score interval half-width (± Elo)7.384.34Δ vs A: -3.04 Elo
LMArena document score (Elo)Unknown1,482.7
Estimated effective USD / 1M tokens$2$8Δ vs A: +6 USD/M
Base input USD / 1M tokens$1.25$4Δ vs A: +2.75 USD/M
Base output USD / 1M tokens$4.25$20Δ vs A: +15.75 USD/M
Base cached-input USD / 1M tokens$0.15$0.4Δ vs A: +0.25 USD/M
Context window (tokens)1,048,5761,050,000Δ vs A: +1,424 tokens
Output limit (tokens)943,718128,000Δ vs A: -815,718 tokens
Input modalitiestext, image, video, file, audiofile, image, text
Model providerMetaOpenAI
Distinct sellers in the catalogue13Δ vs A: +2
Recorded retirement dateUnknownUnknown
7 shared measured facts
Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA Muse Spark 1.2B GPT-5.6 Sol
Benchmark reasoning effortxhighxhigh
Tool useSupportedSupportedΔ vs A: 0
ReasoningSupportedSupportedΔ vs A: 0
Open weightsNot supportedNot supportedΔ vs A: 0
Recorded licenceproprietaryproprietary
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 1.2

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

GPT-5.6 Sol

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$4$20$0.4
> 272,000$8$30$0.8
Migration preflight · compare each candidate with model A

Keeping Muse Spark 1.2 is a valid decision. No pairwise comparison certifies a drop-in replacement.

Muse Spark 1.2 → GPT-5.6 Sol

Known loss or failed requirement

  • Output limit: 943,718 → 128,000 tokens.
  • video input is not supported by the candidate.
  • audio input is not supported by the candidate.
  • 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 1.2

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 1.2Estimated total: $4.625 1,000 full attempts · base rates
  • GPT-5.6 SolEstimated total: $18 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 1.2GPT-5.6 SolCoverage
2,000$4.625Lowest known$18All computed
4,096$7.245Lowest known$26.384All computed
32,768$43.085Lowest known$141.072All computed
131,072$165.965Lowest known$534.288All computed
271,999$342.12375Lowest known$1,097.996All computed
272,000$342.125Lowest known$1,098All computed
272,001$342.12625Lowest known$2,191.008All computed
Vary output; hold input, requests, cache and retries fixed.
Output tokensMuse Spark 1.2GPT-5.6 Sol
128$3.044$10.56
2,048$11.204$48.96
8,192$37.316$171.84

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 1.2 a better-and-cheaper replacement for GPT-5.6 Sol?

Muse Spark 1.2 has an equal-or-higher measured score and an equal-or-lower estimated price. Switching from GPT-5.6 Sol loses capability or fails a requirement. Review the migration checks. The reported score intervals do not overlap; this is not a task-specific success guarantee.

Which model is higher rated: Muse Spark 1.2 or GPT-5.6 Sol?

Muse Spark 1.2 scores higher on LMArena general (1484.9 ±7.4) compared to GPT-5.6 Sol (1457.2 ±4.3), a margin of 27.7 Elo points. The measured confidence intervals do not overlap, demonstrating a statistically significant preference lead on human evaluations.

Which model is more cost-effective: Muse Spark 1.2 or GPT-5.6 Sol?

On a standard balanced workload (3 prompt tokens per 1 generated token), Muse Spark 1.2 costs $2.00 per 1M tokens versus $8.00 for GPT-5.6 Sol. GPT-5.6 Sol is approximately 4.0× the price of Muse Spark 1.2. Base input rates are $1.25 for Muse Spark 1.2 and $4.00 for GPT-5.6 Sol; base output rates are $4.25 for Muse Spark 1.2 and $20.00 for GPT-5.6 Sol.

How do their context windows and generation limits compare?

Muse Spark 1.2 provides a 1,048,576-token context window with a maximum output limit of 943,718 tokens. GPT-5.6 Sol provides a 1,050,000-token context window with a maximum output of 128,000 tokens.

What capabilities are lost when switching between Muse Spark 1.2 and GPT-5.6 Sol?

Switching from Muse Spark 1.2 to GPT-5.6 Sol involves: Output limit: 943,718 → 128,000 tokens; video input is not supported by the candidate; audio input is not supported by the candidate. Conversely, moving from GPT-5.6 Sol to Muse Spark 1.2 involves: Context window: 1,050,000 → 1,048,576 tokens. Run test suites on your specific prompt templates and tool declarations before cutting over production traffic.

Can Muse Spark 1.2 or GPT-5.6 Sol be self-hosted or deployed privately?

Both Muse Spark 1.2 and GPT-5.6 Sol are proprietary cloud models offered via managed API endpoints. Muse Spark 1.2 is served by Meta and 1 catalogue offers, while GPT-5.6 Sol is served by OpenAI and 7 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