Meta AI vs GLM 5.3: Which LLM Is Better at Multi-Step Reasoning with a Large Context Window?
Two 2026 releases now sit at the center of the open-model conversation: Meta's Muse Spark family — the models powering Meta AI's coding and agentic work — and Zhipu AI's GLM-5.3. Both advertise million-token context windows. Both are built for long-horizon, multi-step, agentic work. So which one actually reasons better when you hand it a big problem and a big context?
The contenders
| Muse Spark 1.3 (Meta) | GLM-5.3 (Z.ai) | |
|---|---|---|
| Released | Sep 2, 2026 | Aug 14, 2026 |
| Weights | Proprietary (Open variant: 816B/42B active, open) | Open (~740B) |
| Context window | 1M tokens | 1M tokens |
| Max output | 131K | 128K |
| Inputs | Text, image, video, audio, PDF | Text only |
| Price (in / out per 1M) | $1.25 / $4.25 | $1.40 / $4.40 |
| Reasoning | minimal → max effort levels | Always on (low / high / max) |
Multi-step reasoning: the benchmarks
Multi-step reasoning shows up most clearly in agentic coding benchmarks, where a model must chain dozens of decisions — read an issue, explore a repository, form a plan, write a patch, and verify it against tests:
| Benchmark | GLM-5.3 | Muse Spark |
|---|---|---|
| SWE-bench Verified (real GitHub issues) | 95.4% | 86.6% (1.2) |
| Terminal-Bench 2.1 | 88.2 | 85 (1.3 xhigh) |
| Terminal-Bench 3.0 (long-horizon) | 28.3 | — |
| DeepSWE v1.1 | 66.9 | — |
| Agents' Last Exam | 28.5 | — |
| Tau3-Bench Banking (agentic econ) | — | 52% — #1 of all models (1.3 max) |
| Humanity's Last Exam | ~17.9% (Diamond) | 40.6% |
| GPQA Diamond (science reasoning) | ~88–91% | 89.8% |
On the coding-agent side of multi-step reasoning, GLM-5.3 has the stronger public record: a 9-point SWE-bench Verified lead, top-tier terminal scores, and open-source SOTA on Terminal-Bench 3.0 at release. On general agentic and frontier knowledge tasks, Muse Spark 1.3 strikes back — its 52% on Tau3-Bench Banking was #1 among all models at release, and its 40.6% on Humanity's Last Exam is a top-10 score.
Large context handling
This one is nearly a tie on paper: both offer 1M-token context windows. The differences are in the details:
- GLM-5.3 scores 79.7% on Artificial Analysis's long-context reasoning (AA-LCR) benchmark, and Z.ai built IndexShare specifically for efficient long-context processing. Its long-context category score (65) is among the best tracked.
- Muse Spark matches the 1M window with a long-context category score of 66 (1.3) and ships the practical pitch that "long-running tasks run start to finish in one session." Its multimodal inputs mean the "context" can include video, audio, and PDFs — something GLM-5.3 cannot ingest at all.
Where each one wins
Choose GLM-5.3 if your multi-step work is text-and-code-heavy: repository-scale refactors, terminal agents, long chat histories, or document chains where you also want open weights and self-hosting. It streams ~76 tokens/s and every response includes explicit reasoning.
Choose Muse Spark if your long context is multimodal (video, audio, PDFs), you want the higher general-frontier scores (HLE, Tau3-Bench), or you need speed — 1.3 streams ~190 tokens/s. And Meta's open option, Muse Spark Open (Oct 2026), is the strongest US open-weight model — it even runs on a desktop with two Apple M5 Ultra Mac Studios.
Verdict
For the specific question — multi-step reasoning over a large context window — GLM-5.3 wins on the public numbers: SWE-bench Verified 95.4% vs 86.6%, Terminal-Bench 2.1 88.2 vs 85, and a best-in-class long-context reasoning score (79.7% AA-LCR). It is also the pragmatic pick: open weights, text-first, always-reasoning.
Muse Spark 1.3 is the broader frontier model — multimodal, faster, and ahead on general agentic and knowledge-frontier benchmarks. If your "large context" means a folder of videos and PDFs rather than a wall of code, it's the better tool.
The usual caveat: benchmarks age in weeks, vendor-run evals flatter their own models, and independent numbers vary by harness. Before committing, run both on a slice of your real workload — that's the only benchmark that pays your bills.