Model comparison
GPT-5.5 vs Muse Spark 1.3
OpenAI against Meta, compared on context, price, and verified benchmark results.
Catalog record checked September 5, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
OpenAI
GPT-5.5
Frontier
Meta
Muse Spark 1.3
Frontier
| Specification | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| Provider | OpenAI | Meta |
| Tier | Frontier | Frontier |
| Context window | Winner: 1.05M | 1M |
| Max output | 128K | Winner: 131K |
| Input / 1M tokens | $5 | Winner: $1.25 |
| Output / 1M tokens | $30 | Winner: $4.25 |
| Weights | Closed | Closed |
| Parameters | Not disclosedUnverified | Meta frontier model |
| Reasoning levels | none, low, medium, high, xhigh, max | low, medium, high |
| Modalities | text, image | text, image |
| API model id | gpt-5.5 | muse-spark-1.3 |
| Released | April 23, 2026 | September 2, 2026 |
| Artificial Analysis Intelligence Index [xhigh] (2026-08-14) | 58 | Not verifiedUnverified |
| SWE-bench Verified (2026-09-01) | 82.6 | Not verifiedUnverified |
| Terminal-Bench 2.1 (2026-07-27) | 83.4 | Winner: 88.8 |
| GPQA Diamond (2026-07-27) | 93.5 | Not verifiedUnverified |
| Humanity's Last Exam (2026-04-23) | 52.2 | Not verifiedUnverified |
| DeepSWE 1.1 (2026-09-02) | Not verifiedUnverified | 75.4 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
- 2026-09-03: Meta launch table (transcribed by explainx.ai) (Vendor self-report; absent from the official tbench.ai leaderboard)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-09-02: Meta launch table (Thinking mode: max — self-reported; not broadly available and NOT on the official Datacurve leaderboard)Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task. Comparability: directly comparable
- 2026-09-01: vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only); official SWE-bench not publishedReal GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
- 2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
- 2026-07-27: Kimi K3 model card (vendor-reported, xhigh); announcement charts show 93.6 — minor conflict preservedAgentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-04-23: OpenAI — Introducing GPT-5.5 (vendor, with tools; 41.4 no tools)Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise. Comparability: comparable with caveat — Subset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
Pricing tiers: GPT-5.5: Standard $5/$30 per MTok; >272K input billed 2x in / 1.5x out. Cached input $0.50/MTok; batch $2.50/$15. · Muse Spark 1.3: $1.25/$4.25 per MTok on Meta's Model API (cached input $0.15); a $0.10/$0.20 'contributor' tier applies when your data is used for training. Third-party reports put list at $1.50/$7.50 from 2027-01-01 — confirm before long-term commits.
GPT-5.5
GPT-5.5 is OpenAI's previous flagship — strong on Terminal-Bench 2.0 (82.7%) and 1M-token context.
Best for
- Reasoning
- Agentic coding
- Long-context work
Watch out
Superseded by GPT-5.6 Sol on quality; still a capable, widely integrated model.
Muse Spark 1.3
Muse Spark 1.3 is Meta's September 2026 frontier refresh — a self-reported DeepSWE 1.1 field leader at $1.25/$4.25, with a 1M-token context aimed at autonomous agent workflows.
Best for
- Long-horizon coding agents
- Frontier quality below frontier pricing
- Meta ecosystem
Watch out
The headline 75.4% DeepSWE score comes from the 'max' thinking mode, which is not broadly available yet and is pending independent verification. The 131,072 output cap is per third-party API docs — Meta's own spec page does not publish it. Closed weights, unlike Muse Spark 1.1.
When the cheaper one wins
Muse Spark 1.3 is cheaper on output at $4.25 per million tokens against $30 for GPT-5.5 — about 7.1×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: High
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
- 1 shared named benchmark with differing scores — Measured on: Terminal-Bench 2.1.
- Verified within the last 90 days — Newest catalog check was 6 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- GPT-5.5: OpenAI — Introducing GPT-5.5 (accessed 2026-08-29)
- GPT-5.5: OpenAI API pricing (gpt-5.5 $5/$30) (accessed 2026-08-29)
- Muse Spark 1.3: Meta — Introducing Muse Spark 1.3 (accessed 2026-09-03)
- Muse Spark 1.3: Meta developer — Muse Spark pricing ($1.25/$4.25) (accessed 2026-09-03)
- Muse Spark 1.3: VentureBeat — Muse Spark 1.3 best results need a config developers can't broadly use yet (accessed 2026-09-03)
- Muse Spark 1.3: HaiMaker — muse-spark-1.3 (131,072 max output tokens) (accessed 2026-09-05)
- Muse Spark 1.3: Promptfoo — Meta provider docs (muse-spark-1.3 max_tokens ceiling) (accessed 2026-09-05)
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Diving deeper on one model? GPT-5.5 · Muse Spark 1.3
Common questions
GPT-5.5 vs Muse Spark 1.3
Answered from the verified figures on this page rather than general guidance.
Is GPT-5.5 or Muse Spark 1.3 cheaper for input?
Muse Spark 1.3 is cheaper at $1.25 per million input tokens, against $5 for GPT-5.5 — roughly 4.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.5 has tiered pricing: Standard $5/$30 per MTok; >272K input billed 2x in / 1.5x out. Cached input $0.50/MTok; batch $2.50/$15. Muse Spark 1.3 has tiered pricing: $1.25/$4.25 per MTok on Meta's Model API (cached input $0.15); a $0.10/$0.20 'contributor' tier applies when your data is used for training. Third-party reports put list at $1.50/$7.50 from 2027-01-01 — confirm before long-term commits.
Is GPT-5.5 or Muse Spark 1.3 cheaper for output?
Muse Spark 1.3 is cheaper at $4.25 per million output tokens, against $30 for GPT-5.5 — roughly 7.1× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.5 has tiered pricing: Standard $5/$30 per MTok; >272K input billed 2x in / 1.5x out. Cached input $0.50/MTok; batch $2.50/$15. Muse Spark 1.3 has tiered pricing: $1.25/$4.25 per MTok on Meta's Model API (cached input $0.15); a $0.10/$0.20 'contributor' tier applies when your data is used for training. Third-party reports put list at $1.50/$7.50 from 2027-01-01 — confirm before long-term commits.
Which has the larger context window, GPT-5.5 or Muse Spark 1.3?
GPT-5.5 accepts 1.05M tokens against 1M for Muse Spark 1.3. This only matters if you routinely send very long documents or large codebases.
Do GPT-5.5 and Muse Spark 1.3 support the same reasoning levels?
GPT-5.5 exposes none, low, medium, high, xhigh, max, while Muse Spark 1.3 exposes low, medium, high.
Should I use GPT-5.5 or Muse Spark 1.3?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GPT-5.5 suits reasoning; Muse Spark 1.3 suits long-horizon coding agents.
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: Muse Spark 1.3 costs $1.25 per 1M tokens versus $5 for GPT-5.5 — a 4x difference at the headline tier.
- Context: GPT-5.5 takes 1.05M against 1M for Muse Spark 1.3 — only decisive if your prompts approach the smaller window.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.