What Happened to "Qwen 4.1" and "Llama 5"
During a July 2026 data incident, this page compared GLM 5.2 against a "Qwen 4.1 32B-A3B" and a "Llama 5 405B". Neither could be verified against any official source when the catalog was reconciled in August 2026. The real Qwen lineage runs 3.5 → 3.6 → 3.8, and Meta's open flagship remains Llama 4 Maverick — the next Llama generation is not expected before 2027, and Meta's actual open release of August 2026 is Muse Glimmer 30B (Apache 2.0), a 30B dense multimodal agent model.
The corrected three-way is the real state of the open frontier: GLM 5.2 (Zhipu AI, June 2026), Qwen3.8-Max — Alibaba's hosted flagship, whose open-weights sibling Qwen3.8-2.4T-A95B shipped in August 2026 — and Kimi K3 (Moonshot AI, weights released in late July 2026).
The Three-Way Verdict at a Glance
These three flagships are not competing for the same crown. One wins on license, one on published scores, one on sheer scale — and none of them wins on your Mac.
GLM 5.2
Zhipu's 753B MoE is the only one of the three under a clean MIT license, and it holds the top open-weights SWE-Bench Pro score on Scale's standardized SEAL harness (~62%). The only flagship here the community has run on a Mac at all — 256 GB M3 Ultra territory.
Qwen3.8-Max
The open-weights 2.4T-A95B posts the highest published numbers of the three: 67.7 SWE-Bench Pro and 92.6 GPQA, both vendor-reported. The catch: a custom revenue-threshold license — the first Qwen flagship off Apache 2.0 — and a smallest quant of ~397 GB.
Kimi K3
Moonshot's 2.8T MoE is the largest open-weight model ever released — ~1.56 TB of weights. It is the #1 open model on LiveBench Coding and #3 on the AA Intelligence Index, under an MIT-like license with a $20M model-as-a-service revenue gate. Not Mac-runnable, period.
Scores, With Harness Notes
Every headline number below comes from a different measurement setup, so each row carries its provenance. That is not pedantry — the gap between a vendor scaffold and a standardized harness can be larger than the gap between the models.
| Model | Score | Benchmark | Harness / provenance |
|---|---|---|---|
| GLM 5.2 | 68.5 | SWE-Bench Pro | Zhipu's own agent scaffold |
| GLM 5.2 | ~62 | SWE-Bench Pro | Scale SEAL, standardized — open-weights lead |
| Qwen3.8-2.4T | 67.7 | SWE-Bench Pro | Vendor-reported (Alibaba's setup) |
| Qwen3.8-2.4T | 92.6 | GPQA | Vendor-reported |
| Kimi K3 | #1 open | LiveBench Coding | Public leaderboard |
| Kimi K3 | #3 open | AA Intelligence Index | Artificial Analysis |
Read the two GLM 5.2 rows together and the lesson writes itself: Zhipu's famous 68.5 was produced on Zhipu's own scaffold, while Scale's standardized SEAL rerun puts the best open score near 62 — with GLM 5.2 still on top of the open field. Qwen's 67.7 and Zhipu's 68.5 are therefore not directly comparable: both are vendor numbers from different setups, and Qwen3.8's standardized SEAL results were not yet published when this page was updated. Kimi K3's rankings come from third-party boards, which makes them the most conservative claims in the table.
According to the LLMCheck index, the honest summary is: Qwen3.8-2.4T has the highest published scores, GLM 5.2 has the strongest independently standardized result, and Kimi K3 has the strongest third-party coding ranking. Anyone declaring a single "best open model" without naming the harness is selling something.
License Three-Way: MIT vs Revenue Gates
This comparison used to be the boring section. In August 2026 it is the decisive one, because two of the three flagships shipped with custom terms.
- GLM 5.2 — MIT. Genuinely unrestricted: commercial use, modification, redistribution, closed-product embedding. No caps, no gates. Top license score (10/10) on the LLMCheck methodology.
- Qwen3.8-2.4T — custom revenue-threshold license. The first Qwen flagship to move off Apache 2.0. Weights are downloadable and commercial use is permitted, but a revenue threshold in the terms triggers separate licensing for the largest deployers. Read the exact text before building a business on it.
- Kimi K3 — the "Kimi K3 License". MIT-like in day-to-day effect, with one custom clause: once model-as-a-service revenue built on K3 exceeds $20M per year, a separate agreement with Moonshot is required. For self-hosting and internal use it behaves like MIT; for API resellers it is a real gate.
| License Trait | GLM 5.2 (MIT) | Qwen3.8-2.4T (custom) | Kimi K3 (custom) |
|---|---|---|---|
| Commercial use | Yes, unrestricted | Yes, below threshold | Yes, below gate |
| Revenue / usage gate | None | Revenue threshold | $20M/yr MaaS revenue |
| Weights downloadable | Yes | Yes (~397 GB min quant) | Yes (~1.56 TB) |
| LLMCheck license tier | Top (10/10) | Reduced (custom terms) | Reduced (custom terms) |
If your requirement is zero license ambiguity, GLM 5.2 wins this section outright. The Qwen move matters beyond this comparison: Apache 2.0 was the Qwen line's calling card, and the 3.8 flagship abandoning it is the clearest sign yet that frontier-scale open weights are drifting toward conditional licenses.
Mac Reality: None of the Above
Here is the section that separates this page from a press-release roundup: none of these three flagships is a practical Mac model.
- Kimi K3 — ~1.56 TB of weights. No Mac, and no single consumer machine of any kind, can load it. It is a server-cluster model full stop.
- Qwen3.8-2.4T — smallest quantization ~397 GB. Even a 512 GB M3 Ultra would have no working headroom; the LLMCheck index does not list it as Mac-practical.
- GLM 5.2 — the only one with community Mac runs on record: a 1-bit GGUF at ~22 tok/s on a 256 GB M3 Ultra and a 4-bit MLX build at ~15 tok/s on a 512 GB M3 Ultra (both community-reported; see GLM 5.2 on M3 Ultra).
What Mac users should actually run
The frontier trickles down fast, and the local winners mostly come from these same labs. Updated 24 October 2026: the LLMCheck index’s #1 is Qwen 3.6-35B-A3B (Score 81), which needs only 24 GB, so it is also the index’s pick for a Mac with 32 GB or less.
- Qwen 3.6-35B-A3B — the index’s #1 and its best pick for a Mac with 32 GB or less (Score 81): community64 tok/s community-measured on a 32 GB Mac mini M6, Apache 2.0. Its dense sibling Qwen3.6-27B was the LLMCheck Mac #1 when this was published; it posts 77.2% SWE-bench Verified (vendor-reported) and now ranks #21 (Score 70) at estimated~30 tok/s estimated on an M5 Max.
- DeepSeek V4-Flash — frontier-tier on a 128 GB Mac: its 2-bit MLX build is 96.5 GB, with community-reported ~39 tok/s on an M5 Max.
- Muse Glimmer 30B — Meta's real August 2026 release: Apache 2.0, ~17–18 GB at Q4 for 24–32 GB Macs, vendor-reported 26.6 tok/s on an M5 Max (50.2 with its bundled DFlash drafter).
- Inkling-Small (Thinking Machines Lab, July 2026, Apache 2.0, 276B-A12B) — scores 80.2% on SWE-bench Verified (vendor-reported) and fits a 128 GB Mac Studio via a 2-bit MLX build (88.4 GB); MLX-only for now.
For a ranked list matched to your exact machine, the Best LLM by Mac hub covers every chip and RAM tier.
All three flagships are server-class. To run any of them unquantized, rent a datacenter GPU by the minute on Vast.ai — often 5–6× cheaper than AWS or GCP, with H100s and B200s available on demand.
Vast.ai referral link — we may earn a small commission at no extra cost to you. It never influences our reviews or rankings.
Use-Case Picks
Matching the flagship to the job removes most of the ambiguity:
- Commercial deployment with legal scrutiny — GLM 5.2. MIT with no gates is the only friction-free option among the three.
- Peak published capability via API or your own cluster — Qwen3.8-Max (hosted) or the 2.4T open weights, if the revenue-threshold terms fit your business.
- Agentic coding at frontier scale — Kimi K3 if you have the infrastructure (LiveBench Coding #1 open); GLM 5.2 if you want the standardized-harness leader under MIT.
- Anything on a Mac — none of the three. Run Qwen 3.6-35B-A3B, DeepSeek V4-Flash or Muse Glimmer 30B instead, per the section above.
- Since shipped — Qwen3.8-27B, which was still unreleased when this was written, arrived in August 2026 under Apache 2.0 and is now the index’s strongest dense model by capability (46/50; #14, Score 71), at estimated~29 tok/s estimated on an M5 Max.
The Verdict
The open frontier in August 2026 splits three ways, and the split is about terms and tonnage as much as capability:
- GLM 5.2 wins on license and standardized results. MIT, no gates, and the top open SWE-Bench Pro score on Scale's SEAL harness. It is also the only flagship here with any Mac existence at all — barely, at 256 GB and 1-bit.
- Qwen3.8-Max wins on published scores. 67.7 SWE-Bench Pro and 92.6 GPQA (vendor-reported) lead the table, but the custom revenue-threshold license and 397 GB minimum footprint make it an API-or-cluster proposition.
- Kimi K3 wins on scale and third-party coding rank. The largest open-weight model ever, #1 open on LiveBench Coding — and, at 1.56 TB, the clearest possible reminder that "open weights" and "runnable" are different words.
If you take one thing away: the open frontier's top end has left consumer hardware behind, and the licenses are following. The models Mac users actually run — Qwen3.6-27B, DeepSeek V4-Flash, Muse Glimmer 30B — are distillations and mid-sizers from the same labs, and according to the LLMCheck index that is where the local value lives. The leaderboard ranks all of them side by side.