Muse Spark 1.3 vs GPT-5.6 vs Claude Opus 5: 2026 AI model benchmark
Muse Spark 1.3 beats GPT-5.6 Sol on coding (75.4% DeepSWE 1.1), matches Claude Opus 5 on agentic (88.8% Terminal-Bench 2.1), leads on long-context (98.5%/98.1%). Which model to pick.
Meta Muse Spark 1.3 leads GPT-5.6 Sol by ~5 points and matches Claude Opus 5 across the three benchmarks that matter most for production work in September 2026: coding (DeepSWE 1.1), terminal / agentic (Terminal-Bench 2.1), and long-context retrieval. Spark 1.3 sets the new state-of-the-art on long-context retrieval (98.5% at 256K-512K tokens; 98.1% at 512K-1M tokens). GPT-5.6 still leads on multimodal breadth (audio + video), and Claude Opus 5 retains a narrow edge on agentic terminal workflows. This article compares all three across 9 dimensions and gives a per-workload recommendation.
Quick answers: Muse Spark 1.3 vs GPT-5.6 vs Claude Opus 5
| Question | Answer |
|---|---|
| Best for coding (Sept 2026)? | Muse Spark 1.3 — 75.4% DeepSWE 1.1, +5 points vs GPT-5.6 Sol |
| Best for terminal / agentic? | Tied — Spark 1.3 and Claude Opus 5 at 88.8% / ~89% Terminal-Bench 2.1 |
| Best for long-context (1M+ tokens)? | Muse Spark 1.3 — 98.1% retrieval vs ~94% / ~95% for GPT-5.6 / Claude Opus 5 |
| Best for multimodal (audio + video)? | GPT-5.6 Sol — only model with audio + video native input |
| Cheapest API? | GPT-5.6 — $15/$60 per million input/output (Spark 1.3 TBD; Opus 5 $15/$75) |
| Best for on-device / privacy? | Muse Glimmer — open-weight, runs locally |
| Best for consumer agent (chat + booking)? | Meta Muse agent — first consumer agent shipped at scale |
| Open weights? | None — Spark 1.3, GPT-5.6, and Opus 5 are all closed |
For pure coding + agentic + long-context, Spark 1.3 is the strongest. For multimodal or chat, GPT-5.6. For agentic workflows that touch shell / file system, Claude Opus 5.
What are the benchmark scores for each model?
Muse Spark 1.3 leads on DeepSWE 1.1 (coding) and long-context retrieval. GPT-5.6 Sol leads on multimodal. Claude Opus 5 ties on Terminal-Bench 2.1 (agentic).
Coding: DeepSWE 1.1
| Model | Score | Notes |
|---|---|---|
| Meta Muse Spark 1.3 | 75.4% | Beats GPT-5.6 by ~5 points |
| Anthropic Claude Opus 5 | ~76% | Narrow lead on Spark 1.3 (~0.5 points) |
| OpenAI GPT-5.6 Sol | ~70% | Lowest of the three |
DeepSWE 1.1 is the September 2026 update to the SWE-bench family — a curated set of real GitHub issues graded on whether the model's patch passes the project's test suite. The ~5-point gap between Spark 1.3 and GPT-5.6 Sol is large in this benchmark family — SWE-bench scores typically move 1-2 points per generation.
Terminal / agentic: Terminal-Bench 2.1
| Model | Score | Notes |
|---|---|---|
| Anthropic Claude Opus 5 | ~89% | Narrow lead over Spark 1.3 |
| Meta Muse Spark 1.3 | 88.8% | Matches Opus 5 within noise |
| OpenAI GPT-5.6 Sol | ~85% | Behind on shell-style tasks |
Terminal-Bench 2.1 grades models on real shell tasks: install dependencies, edit configs, run tests in a sandboxed environment. Claude Opus 5 has historically led this benchmark — Spark 1.3 closing the gap to ~0.2 points is Meta's biggest benchmark win.
Long-context retrieval
| Context length | Muse Spark 1.3 | GPT-5.6 Sol | Claude Opus 5 |
|---|---|---|---|
| 256K-512K tokens | 98.5% | ~96% | ~97% |
| 512K-1M tokens | 98.1% | ~94% | ~95% |
Long-context retrieval measures whether the model can find a specific fact buried in a 1M-token document. Spark 1.3 leads both buckets by 2-4 points. This matters for legal, financial, and code-repo workflows where documents exceed 256K tokens.
General knowledge: MMLU-Pro
| Model | Score |
|---|---|
| GPT-5.6 Sol | ~92% |
| Claude Opus 5 | ~91% |
| Muse Spark 1.3 | ~89% |
On general-knowledge benchmarks, Muse Spark 1.3 trails by 2-3 points. Meta's positioning is that Spark is for production workloads (coding, agentic, long-context), not trivia — the MMLU gap reflects that focus.
Multimodal (text + image + audio + video)
| Model | Inputs supported |
|---|---|
| OpenAI GPT-5.6 Sol | Text + image + audio + video (native, all four) |
| Muse Spark 1.3 | Text + image |
| Claude Opus 5 | Text + image |
Only GPT-5.6 supports native audio and video input. If your workload is voice transcription or video understanding, GPT-5.6 is the only choice.
Math: MATH-Hard
| Model | Score |
|---|---|
| Claude Opus 5 | ~95% |
| GPT-5.6 Sol | ~93% |
| Muse Spark 1.3 | ~91% |
Claude Opus 5 retains the math lead. Spark 1.3 is ~4 points behind on competition-math problems.
Code translation (Polyglot)
| Model | Score |
|---|---|
| Muse Spark 1.3 | ~78% |
| GPT-5.6 Sol | ~74% |
| Claude Opus 5 | ~73% |
Polyglot measures cross-language code translation (Python ↔ Rust ↔ TypeScript ↔ Go). Spark 1.3 leads here by 4-5 points.
How do the three models compare on price?
Pricing as of September 22, 2026 (per million tokens):
| Model | Input | Output | Cache read | Notes |
|---|---|---|---|---|
| OpenAI GPT-5.6 Sol | $15 | $60 | $7.50 | Cheapest per-token |
| Anthropic Claude Opus 5 | $15 | $75 | $7.50 | Output is the most expensive |
| Meta Muse Spark 1.3 | TBD | TBD | TBD | Not disclosed at launch (Sept 3, 2026) |
Meta has not published Spark 1.3 per-token rates as of Sept 22. Per developer reports, the actual rates are likely $12-18 input / $50-70 output — competitive with GPT-5.6 — but Meta has stated pricing will be finalized when general availability goes beyond the limited Muse Code IDE cohort.
For cost-sensitive production use, GPT-5.6 Sol is the cheapest fully-disclosed option.
Token efficiency: which model needs the most tokens?
Muse Spark 1.3 is ~25% more token-efficient than Spark 1.2 and uses fewer tokens than GPT-5.6 / Claude Opus 5 on equivalent coding tasks. Meta's published comparison:
| Task | Spark 1.3 tokens | GPT-5.6 tokens | Opus 5 tokens |
|---|---|---|---|
| Implement a function from spec | 1,200 | 1,800 | 1,650 |
| Debug a failing test | 800 | 1,100 | 1,050 |
| Refactor a class | 2,400 | 3,200 | 2,950 |
Per Spark 1.3 vs Spark 1.2 (same task completion): 25% fewer tokens + 20% fewer tool calls. Combined effect: Spark 1.3 is meaningfully cheaper per task than its predecessor.
Which model should I use for which workload?
| Workload | Recommended | Why |
|---|---|---|
| Coding agent (SWE-bench style) | Muse Spark 1.3 | 75.4% DeepSWE 1.1, code-translation lead |
| Terminal / shell / DevOps | Tied: Spark 1.3 / Opus 5 | 88.8% / ~89% Terminal-Bench 2.1 |
| Long-context doc analysis (1M+ tokens) | Muse Spark 1.3 | 98.1% retrieval, beats both |
| Multimodal (audio + video) | GPT-5.6 Sol | Only model with native audio + video |
| Math / formal reasoning | Claude Opus 5 | ~95% MATH-Hard, leads |
| General chat / writing | GPT-5.6 Sol | Best MMLU-Pro, broadest training |
| Cheapest per-token | GPT-5.6 Sol | $15/$60 |
| On-device / privacy | Muse Glimmer | Open-weight, runs locally |
| Consumer agent | Meta Muse agent | First agent at Meta's scale |
| Code translation (cross-language) | Muse Spark 1.3 | Polyglot lead |
For most production coding workflows in September 2026, Muse Spark 1.3 is the strongest default. Switch to GPT-5.6 if you need audio/video. Switch to Claude Opus 5 if you need the absolute best agentic terminal performance or math reasoning.
What changed since each model launched?
| Model | Launched | Key update since |
|---|---|---|
| OpenAI GPT-5.6 Sol | August 15, 2026 | Sol variant tuned for solo agentic coding (vs GPT-5.6 base) |
| Anthropic Claude Opus 5 | August 20, 2026 | Computer-use beta; Opus 5.1 rumor for Q4 2026 |
| Meta Muse Spark 1.3 | September 2, 2026 | Paid API opened Sept 3; pricing TBD |
All three shipped within a 30-day window in mid-2026. The competitive landscape is closer than at any point since GPT-3 vs Claude 1 — any of the three is "good enough" for most production tasks.
What's next for the three labs?
| Lab | Next planned launch | Date |
|---|---|---|
| Meta | Watermelon (multimodal flagship) | Q2-Q3 2027 |
| OpenAI | GPT-5.7 + new agentic tools | Q4 2026 |
| Anthropic | Claude Opus 5.1 | Q4 2026 (rumored) |
The next 12 months will likely see all three labs ship multimodal reasoning (text + image + audio + video + spatial) and longer context windows (2M+ tokens). Meta's Watermelon is positioned as the GPT-6-class competitor.
Bottom line: Muse Spark 1.3 is the strongest model for coding + long-context in September 2026. GPT-5.6 is the cheapest and only multimodal option. Claude Opus 5 ties on agentic and leads on math. For most production coding workflows, default to Muse Spark 1.3.
For the Muse personal agent (consumer rollout), see our Muse agent coverage. For an overview of the full Muse lineup, see our Muse umbrella FAQ.
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Sources
(5 references)- [1]Meta Newsroom — Muse Spark 1.3 benchmark release (Sept 2 2026)(2026-09-02)↩
- [2]IBTimes — Meta Launches Muse Spark 1.3 (Sept 3 2026)(2026-09-03)↩
- [3]Sina Tech — Meta 发布最强 AI 模型 Muse Spark 1.3 (Sept 3 2026)(2026-09-03)↩
- [4]OpenAI — GPT-5.6 launch notes(2026-08-15)↩
- [5]Anthropic — Claude Opus 5 system card(2026-08-20)↩