The short version: if you're routing agentic coding work and want the highest reported benchmark score plus the most permissive license, default to GLM-5.2's MIT weights; if your agent needs to reason over screenshots or video inside the same loop, MiniMax M3 is the only one of the three built with native multimodality; if you're already on GitHub Copilot's model picker and want the cheapest cached-input rate, Kimi K2.7 Code slots in with the least friction. None of the three should be picked on score alone — the gap between 62.1% and 58.6% on SWE-bench Pro is smaller than the gap between their licenses.

Three different bets, one benchmark#

Z.ai, MiniMax, and Moonshot AI all shipped open-weight coding models within a six-week window this summer, and all three are chasing the same SWE-bench-Pro-class frontier — but they made different architectural bets to get there. GLM-5.2 bet on scale and cost: a roughly 744-billion-parameter MoE with about 40 billion active per token, released under a pure MIT license that VentureBeat reported running at roughly one-sixth the cost of GPT-5.5 on long-horizon coding runs. MiniMax M3 bet on breadth: a new MiniMax Sparse Attention architecture built to hold a full 1-million-token context while natively handling images and video in the same forward pass — the first open-weight model to combine all three, per MarkTechPost's launch coverage. Kimi K2.7 Code bet on efficiency inside an existing lineage: built directly on the K2.6 architecture, Moonshot's release reports roughly 30% lower reasoning-token usage than its predecessor, trading a smaller 256K context window for tighter agentic loops.

The gap between 62.1% and 58.6% on SWE-bench Pro is smaller than the gap between an MIT license and one that requires a commercial agreement.

Score is the least useful column#

If you're routing purely on the headline number, GLM-5.2's vendor-reported 62.1% on SWE-bench Pro is the highest of the three, ahead of MiniMax M3's 59.0% and Kimi K2.7 Code's 58.6%. But single-digit deltas on a benchmark this new should be treated as noise until independent harnesses converge — and Kimi K2.7 Code has already drawn skepticism on this point. VentureBeat's follow-up coverage noted that practitioners say some of Moonshot's reported gains don't hold up outside the vendor's own test harness, even as Moonshot's Kimi Code Bench v2 numbers claim a 21.8% jump over K2.6. That doesn't make K2.7 Code a bad router target — it means you should validate on your own repo before committing agent spend to any of these three, not just the one with the asterisk.

Context and multimodality are the real forks#

The more durable differentiator is architecture, not score. GLM-5.2 and MiniMax M3 both advertise roughly 1-million-token context windows, useful for whole-repo or whole-monorepo agentic tasks where you don't want to chunk retrieval. Kimi K2.7 Code caps out at 256K tokens — plenty for most single-service coding agents, but a real constraint if your routing layer needs to hold an entire large codebase in context at once.

Multimodality is where MiniMax M3 stands alone. GLM-5.2, despite occasional marketing claims, is a text/code model at its core — Z.ai's actual vision-capable model is a separate release, GLM-5V-Turbo, and community reports confirm GLM-5.2 itself cannot process images natively the way M3 does. If your agentic coding pipeline includes visually verifying a rendered UI, reading a design mock, or parsing a screen-recording bug report, M3 is architecturally the only one of the three built for that loop without bolting on a separate vision call.

The license line matters more than it looks#

This is the fork that gets skipped in most model-vs-model writeups, and it shouldn't be. GLM-5.2 ships under a pure MIT license — no separate commercial agreement, full self-hosting rights (we walked through why that open-weight release was a step change for agentic coding). Kimi K2.7 Code ships under a Modified MIT license, with full weights published to Hugging Face on day one. MiniMax M3 is open-weight but not fully open-source: a Hugging Face discussion thread on the model's license terms confirms M3 ships under the MiniMax Community License, which requires a separate commercial agreement for any revenue-generating deployment — a step down in permissiveness from even MiniMax's own prior M2.7 release. For a solopreneur or small team planning to self-host and resell agent output, that's not a footnote — it's a legal review step GLM-5.2 and Kimi K2.7 Code don't require.

Price follows a similar shape. Kimi K2.7 Code's OpenRouter listing shows a steep cache discount — roughly $0.95 per million input tokens on a cache miss versus $0.19 on a hit — which rewards agent loops that repeat large system prompts or tool schemas across turns. GLM-5.2's list pricing runs lower on output tokens across most providers, which matters more for coding agents that generate long diffs and commit messages. MiniMax M3 lands in the middle at roughly $0.60/$2.40 per million tokens, a price that has to also cover the multimodal input path competitors don't offer at all.

The decision#