Compare / head-to-head
GLM-5.3vsMAI-Thinking-1
GLM-5.3 leads 2 of 2 shared benchmarks. GLM-5.3 has the larger context window (1M tokens).
Benchmarks from cited public sources; pricing from official pages; status from official provider feeds.
Shared benchmarks
2 – 0
GLM-5.3 leads
Cheaper per token
—
list price, input + output
Larger context
GLM-5.3
1M tokens
Provider uptime (30d)
— · —
Z.ai · Microsoft
GLM-5.3
Z.ai · released 2026-08-14
text
MAI-Thinking-1
Microsoft · released 2026-08-12
text
- Context
- 256K
- Max out
- 64K
- Input /1M
- —
- Output /1M
- —
- Cached /1M
- —
- Scores
- 15 · 7 core
Quality
Benchmark matrix
2 shared · 22 only GLM-5.3 · 13 only MAI-Thinking-1| Benchmark | GLM-5.3 | MAI-Thinking-1 | Δ | Edge |
|---|---|---|---|---|
| Reported by both · 2 | ||||
| GPQA Diamond | 90.9% ↗epoch | 84.2% ↗ | +6.7 pt | GLM-5.3 |
| SimpleQA Verified | 41% ↗epoch | 31% ↗ | +10 pt | GLM-5.3 |
| Only GLM-5.3 reports · 22 | ||||
| Agents' Last Exam | 28.5% ↗ | not reported | — | — |
| Agents' Last Exam (ALE-CLI) | 28.5% ↗ | not reported | — | — |
| AutomationBench v1.0.6 | 48.2% ↗ | not reported | — | — |
| CyberGym | 84.5% ↗ | not reported | — | — |
| DeepSWE v1.1 | 66.9% ↗ | not reported | — | — |
| ExploitBench | 54.4% ↗ | not reported | — | — |
| ExploitGym 2h | 105 ↗ | not reported | — | — |
| ExploitGym 6h | 130 ↗ | not reported | — | — |
| FrontierMath Tier 4 v2 (Epoch AI run) | 29.3% ↗epoch | not reported | — | — |
| FrontierMath Tiers 1-3 v2 (Epoch AI run) | 68.8% ↗epoch | not reported | — | — |
| FrontierSWE | 78.1% ↗ | not reported | — | — |
| GDPval-AA v2 | 1769 ↗ | not reported | — | — |
| HLE (with tools) | 62.5% ↗ | not reported | — | — |
| NL2Repo | 58% ↗ | not reported | — | — |
| OTIS Mock AIME 2024-2025 (Epoch AI run) | 91.1% ↗epoch | not reported | — | — |
| PostTrainBench | 39.8% ↗ | not reported | — | — |
| ProgramBench (Almost Solved) | 19% ↗ | not reported | — | — |
| SWE-Marathon v1.1 | 42.5% ↗ | not reported | — | — |
| Terminal-Bench 2.1 | 88.2% ↗ | not reported | — | — |
| Terminal-Bench 3.0 | 28.3% ↗ | not reported | — | — |
| Toolathlon Verified | 73% ↗ | not reported | — | — |
| Z.ai Code Bench (max effort) | 34.5% ↗ | not reported | — | — |
| Only MAI-Thinking-1 reports · 13 | ||||
| SWE-bench Verified | not reported | 73.5% ↗ | — | — |
| MMLU-Pro | not reported | 85% ↗ | — | — |
| AIME 2025 | not reported | 97% ↗ | — | — |
| AIME 2026 | not reported | 94.5% ↗ | — | — |
| AdvancedIF | not reported | 85% ↗ | — | — |
| BFCL v3 | not reported | 72% ↗ | — | — |
| GraphWalks (<=128K) | not reported | 90% ↗ | — | — |
| HMMT February 2026 | not reported | 84.9% ↗ | — | — |
| IFBench | not reported | 69% ↗ | — | — |
| LiveCodeBench v6 | not reported | 87.7% ↗ | — | — |
| MultiChallenge | not reported | 53% ↗ | — | — |
| SWE-bench Pro | not reported | 52.8% ↗ | — | — |
| Terminal-Bench 2.0 | not reported | 46% ↗ | — | — |
Scores tagged "epoch" or "matharena" are independent runs, used only where the lab has not published its own; ⚠ marks rows MathArena flags as released after the competition. Higher is better on every row. Δ is GLM-5.3 minus MAI-Thinking-1 in the benchmark's own unit. "Not reported" means the lab has not published that figure; it is not a zero. ↗ opens the source.
Specs & pricing
Side by side
Official pricing pages and model cards| Spec | GLM-5.3 | MAI-Thinking-1 | Edge |
|---|---|---|---|
| Context window | 1M tokens | 256K tokens | GLM-5.3 |
| Max output | 131K tokens | 64K tokens | GLM-5.3 |
| Input price / 1M | $1.4 ↗ | — | — |
| Output price / 1M | $4.4 ↗ | — | — |
| Cached input / 1M | $0.26 ↗ | — | — |
| Input + output / 1M Lower is cheaper. List prices; batch, tool and regional fees excluded. | $5.8 | — | — |
| Modalities | text | text | Tie |
| Released | 2026-08-14 | 2026-08-12 | — |
| Cited benchmark scores | 33 | 15 | — |
Reliability
Provider status
Last 60 days · refreshed daily on this page · live on /statusMore matchups
GLM-5.3 vs …
Models sharing the most benchmarksMore matchups
MAI-Thinking-1 vs …
Models sharing the most benchmarksBuilt by Respan
Which one wins on your data?
Public benchmarks are a starting point. Run GLM-5.3 and MAI-Thinking-1 on your own prompts with Respan evals, or route to either through one gateway key with automatic failover.