GLM 5 vs MAI-Thinking-1
Z.ai · China | Microsoft · US · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%) or microsoft's first in-house flagship reasoner, trained without openai distillation. Choose GLM 5 if you need self-hosting or data privacy; MAI-Thinking-1 if you want a managed API.
GLM 5 (Z.ai, China) and MAI-Thinking-1 (Microsoft, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Cost model: GLM 5 ships open weights you can self-host (hardware cost only, no per-token fee), while MAI-Thinking-1 is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: MAI-Thinking-1 holds 1.3× more — 256K (~384 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: MAI-Thinking-1 is the newer model by about 6 months (released August 12, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | GLM 5 | MAI-Thinking-1 |
|---|---|---|
| Provider | Z.ai (China) | Microsoft (US) |
| Released | February 12, 2026 | August 12, 2026 |
| Context window | 200K (~300 pages) | 256K (~384 pages) |
| Price (in/out) | $1/$3.2 per 1M tokens | Not published |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, code | text, code |
| SWE-Bench Verified | 77.8% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and its weights are open while MAI-Thinking-1 is API-only.
Complex systems design and backend reasoning
GLM 5
GLM 5 lists complex systems design and backend reasoning among its strengths; MAI-Thinking-1 does not.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks among its strengths; MAI-Thinking-1 does not.
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)
MAI-Thinking-1
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence — and it carries the larger 256K context.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation
MAI-Thinking-1
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence — and it is the newer of the two.
Efficient reasoning at low token cost for its class
MAI-Thinking-1
Its 256K window holds about 1.3× more than GLM 5's 200K in a single prompt.
Lowest cost at scale
MAI-Thinking-1
Its weights are open, so at volume you pay for your own hardware instead of GLM 5's $1/$3.2 per 1M tokens.
Largest single-prompt input
MAI-Thinking-1
Its 256K window is about 1.3× larger than GLM 5's 200K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MAI-Thinking-1
At Not published it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MAI-Thinking-1
Larger 256K window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ GLM 5
Open weights let you run it on your own hardware; MAI-Thinking-1 is API-only.
Anyone whose priority is agentic planning and long-horizon coding workflows
→ GLM 5
It is specifically built for that.
Anyone whose priority is very strong math reasoning (aime 2025 97%, aime 2026 94.5%)
→ MAI-Thinking-1
That is its strongest area.
An enterprise with regional data-residency rules
→ MAI-Thinking-1 or GLM 5
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget price band.
MAI-Thinking-1: where it fits
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Released August 12, 2026 by Microsoft, it is built for very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%), microsoft's first in-house flagship reasoner, trained without OpenAI distillation, efficient reasoning at low token cost for its class, and competitive with Claude Opus 4.6 on SWE-Bench Pro (vendor-reported).
Its trade-offs: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. MAI-Thinking-1 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both GLM 5 and MAI-Thinking-1 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is GLM 5 or MAI-Thinking-1 better for coding?
Public SWE-Bench figures are not available for MAI-Thinking-1, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows while MAI-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or MAI-Thinking-1?
GLM 5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-Thinking-1 is API-metered at Not published. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
MAI-Thinking-1 — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you GLM 5, MAI-Thinking-1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GLM 5 or MAI-Thinking-1?
MAI-Thinking-1 — released August 12, 2026, about 6 months after GLM 5.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.