If you want the best open-weight coding model in November 2026, start where you did in September: at your GPU, not the leaderboard. On a single 24GB card, Qwen3-Coder-30B-A3B is still the best coder that fits (~19–22GB at Q4_K_M, Apache-2.0). On a multi-GPU node or a rented cluster, GLM-5.3 is the strongest open coder by the benchmarks labs now actually report. DeepSeek V4-Pro is still the name people quote for topping SWE-bench Verified. That top-line ranking barely moved in two months — which is itself the story. What moved underneath it did not: the scoreboard, the frontier, and two licenses we got wrong.
This is the November cut of our September open-weight coding ranking and the October license-and-hardware map — read those for the full field. Here is what actually changed.
1. The benchmark you were ranking on is a solved test#
The single most useful thing to know this month is that SWE-bench Verified has effectively saturated. The serious open models now cluster under an ~80% ceiling, and when a benchmark saturates, a two-point gap stops separating models and starts measuring test noise and contamination. The labs know it: GLM's own card leads with Terminal-Bench 2.1 (81.0) and SWE-bench Pro (62.1), not a Verified score, and GLM-5.3 headlines open-source SOTA on Terminal-Bench 3.0. MiniMax reports its MIT-licensed M2 against SWE-bench Verified at 69.4 on its own harness — and even that is framed against an older peer set.
When every candidate has beaten the test, the test can't tell you which one to ship. The leaderboard didn't get less important because models got worse. It got less important because they all got good.
The practical move: use SWE-bench Verified as a floor — is this model in the serious tier at all? — then rank the finalists on the harder, less-saturated boards the labs have moved to (Terminal-Bench and SWE-bench Pro for agentic/terminal work; DeepSWE and SWE-Marathon for long-horizon tasks), and above all on your repository. A model that tops a saturated board is not the same as a model that fixes your bugs.
2. What actually shipped since September#
Two genuinely new frontier open-weight models landed in late October, both too large to self-host casually but both real:
- Mistral Large 4 "Le Chonk" — Mistral open-weighted a ~1-trillion-parameter MoE on October 29 (FP8 and NVFP4 checkpoints), positioning it as a top open model for coding and cyber defense; The Next Web confirmed the trillion-parameter weights release. Mistral reports Terminal-Bench 4.0 climbing to ~41% at release from ~28% at preview — vendor numbers, so treat them as directional. This is the model that supersedes the Devstral-era Mistral entry in our old ranking.
- Meta Muse Spark Open — Meta shipped an 816B-parameter MoE (~42B active, 1M context, multimodal) on October 15, notable less for a benchmark than for how you run it: around 1TB of memory, which Meta demoed across two 512GB M5 Ultra Mac Studios. It's "open" in license and emphatically not in reach.
On the incremental side, DeepSeek has been shipping a faster V4.1-Flash tier (a September release, refreshed as V4.1-Flash-1017 in October per DeepSeek's changelog) — useful if you call DeepSeek by API, not a new self-hosting option.
3. Two licenses we have to correct#
We checked the primary license files this time, and two entries in our earlier rankings were wrong:
- GLM ships its code under Apache-2.0, but the GLM-5.3 weights carry a custom Z.ai license reported to include a "hyperscaler clause" for very large model-as-a-service providers. That is not the clean MIT we listed in September.
- Kimi K3 weights are under a bespoke "Kimi K3 License" — permissive in substance, but a named custom license, not the "modified MIT" we wrote. (That modified-MIT belongs to the older Kimi K2.5; the confusion was ours.)
For a solo product, either is almost certainly fine. But if you're building model-as-a-service or deploying at real scale, the license column still outranks the last benchmark point. The genuinely unrestricted picks remain Apache-2.0 (Qwen3-Coder, gpt-oss) and MIT (DeepSeek, MiniMax-M2) — read the model card before you commit.
4. What to run, by hardware (still the right question)#
- One consumer GPU (16–24GB): Qwen3-Coder-30B-A3B (~19–22GB at 4-bit) or gpt-oss-20b (~16GB). Unchanged since September; nothing new displaced them. Serve via Ollama, LM Studio, or Jan (comparison here) and point your editor at the local endpoint.
- One 80GB card: gpt-oss-120b fits on a single H100/MI300X.
- Multi-GPU node, or rent by the hour: GLM-5.3, Kimi K3, Mistral Large 4, DeepSeek V4-Pro. Unless you keep the metal busy, renting beats owning.
- A pair of Mac Studios: Meta Muse Spark Open, if you have ~1TB of unified memory lying around.
Bottom line: the ranking is stable, which is the headline, not the footnote. Own Qwen3-Coder-30B-A3B on one card; serve GLM-5.3 on a cluster; reach for the API for the trillion-parameter tier. Pick by hardware and license first, and stop treating a saturated benchmark as a tiebreaker. If you'd rather not host at all, our coding-model leaderboard and coding-agent ranking cover the proprietary APIs and the tools that drive these weights.
