GLM-5.1 vs Qwen3.6 Plus vs MiniMax M2.7: In‑Depth 2026 Review of China’s Top AI Models

1. Model Overview

MiniMax M2.7 (released March 2026) is positioned as an all‑round Agent platform with MoE sparse expert architecture, 230 B parameters (≈10 B active), ~200 K token context, self‑evolution and low hallucination. GLM‑5.1 (released 7 April 2026) stems from Tsinghua research, targets Chinese use‑cases and long‑running engineering agents, and uniquely supports 8 hours of autonomous task execution. Qwen3.6 Plus (released 2 April 2026) emphasizes cost‑effectiveness, open‑source ecosystem integration, and a MoE design with native 256 K (extendable to 1 M) token context, the longest among domestic models.

2. Benchmark Performance

2.1 Knowledge & Chinese Understanding

On MMLU (57 subjects) GLM‑5.1 scores 82 %, Qwen3.6 Plus 83 %, MiniMax M2.7 85 %. On C‑Eval (Chinese) GLM‑5.1 leads with 90 % versus 89 % (Qwen) and 88 % (M2.7). GPQA Diamond results are 86.2 (GLM‑5.1), 90.4 (Qwen), 87 (M2.7). The conclusion: all three are on a similar level, with GLM‑5.1 slightly better at Chinese semantics, Qwen stronger on professional knowledge, and M2.7 balanced.

2.2 Mathematics & Logical Reasoning

GSM8K (elementary math) – GLM‑5.1 88 %, Qwen Plus 90 %, M2.7 92 %. AIME (advanced math) – GLM‑5.1 94.0, Qwen Plus 95.30, M2.7 81.0. HLE (composite reasoning + tools) – GLM‑5.1 52.3, Qwen Plus 50.6, M2.7 28. The conclusion: M2.7 excels on routine math, Qwen on high‑level competition math, while GLM‑5.1 is most reliable for complex, tool‑augmented reasoning.

2.3 Code & Engineering Ability

HumanEval (basic coding) – GLM‑5.1 75 %, Qwen Plus 76 %, M2.7 78 %. SWE‑Bench Pro (real‑world GitHub bug fixing) – GLM‑5.1 58.4 % (global #1, surpassing GPT‑5.4 and Claude Opus 4.6), Qwen Plus 56.6 %, M2.7 56.22 %. Terminal‑Bench 2.0 – GLM‑5.1 69 % (global #1), Qwen Plus 61.6 %, M2.7 57.0 %. NL2Repo (repo generation) – GLM‑5.1 42.7, Qwen Plus not reported, M2.7 39.8. Conclusions: GLM‑5.1 leads engineering‑level code generation, Qwen is strong for rapid development, M2.7 is adequate for everyday coding.

2.4 Long‑Task Capability

GLM‑5.1 can autonomously iterate for 8 hours, building a full Linux desktop with 1 200+ steps and delivering a PR by the next workday. Qwen Plus auto‑splits complex tasks, completing a full corporate website in 8 minutes at a cost of ¥0.15. MiniMax M2.7 supports 1‑2 hour long‑tasks, with its native Agent Harness framework handling up to 100 iteration cycles. Conclusion: GLM‑5.1 dominates ultra‑long engineering tasks, Qwen offers the best cost‑performance for fast agent development, and M2.7 provides continuous self‑evolution.

2.5 Multimodal Ability

On a multimodal composite score (out of 10) GLM‑5.1 scores 6.5, Qwen Plus 9.6, MiniMax M2.7 8.8. Specific dimensions: image‑text – Qwen Plus 91.2 % vs GLM‑5.1 “basic” and M2.7 “good”; OCR – Qwen Plus 83.4 % vs GLM‑5.1 “basic”; spatial intelligence – Qwen Plus 96.9 % vs GLM‑5.1 “average”; video understanding – Qwen Plus “strong” vs GLM‑5.1 “weak”. Conclusion: Qwen Plus is the clear multimodal leader, GLM‑5.1 is adequate for text‑centric tasks, M2.7 offers balanced multimodal performance.

3. Cost and Ecosystem

3.1 API Pricing

MiniMax M2.7: $0.30 per 1M input tokens, $1.20 per 1M output tokens (lowest overall). Qwen Plus: $0.50 / $3.00 (medium). GLM‑5.1: $1.40 / $4.40 (higher). Claude Opus 4.6 (reference) costs $15 / $75. M2.7’s price is roughly 1/20 of Claude’s, giving it an extreme cost advantage.

3.2 Open‑Source & Deployment

GLM‑5.1 is fully MIT‑licensed, supports local deployment, and is free for commercial use. MiniMax M2.7 provides open weights on HuggingFace, supports local deployment but requires written authorization for commercial use. Qwen 3.6 Plus is API‑only, no local deployment, but can be used commercially via Alibaba Cloud Bailei.

3.3 Ecosystem & Tooling

GLM‑5.1 offers strong developer ecosystem (Claude Code/OpenClaw adapters) and high toolchain maturity. Qwen Plus has the most complete ecosystem (HuggingFace + ModelScope) and the highest toolchain maturity. MiniMax M2.7 benefits from rapid ecosystem expansion and native OpenClaw support. For Chinese‑chip compatibility, both GLM‑5.1 and MiniMax M2.7 support Huawei Ascend Day 0, while Qwen Plus has broader support.

4. Composite Scores & Scenario Recommendations

Overall 10‑point scores: GLM‑5.1 8.8, Qwen Plus 9.0, MiniMax M2.7 9.2. Code engineering: GLM‑5.1 9.5 (best), Qwen Plus 9.0, M2.7 8.7. Math reasoning: Qwen Plus 9.2 (best), GLM‑5.1 8.8, M2.7 8.5. Multimodal: Qwen Plus 9.6 (best), M2.7 8.8, GLM‑5.1 6.5. Cost‑performance: MiniMax M2.7 9.5 (top), GLM‑5.1 8.0, Qwen Plus 8.5. Open‑source ecosystem: GLM‑5.1 9.4, Qwen Plus 7.5, M2.7 8.0.

Scenario guidance:

Choose GLM‑5.1 for long‑duration engineering projects, fully open‑source/commercial use, high‑security domains, or GPU/kernel optimization.

Choose Qwen Plus for multimodal document/OCR/vision tasks, ultra‑long context (up to 1 M tokens), high‑throughput production, or when a mature plugin ecosystem is required.

Choose MiniMax M2.7 for extreme cost‑sensitivity (≈1/20 of Claude), general office/content creation, self‑evolution needs, or deep OpenClaw users.

5. Final Takeaways

All three models have entered the global top tier, narrowing the gap with GPT‑5.4 and Claude Opus 4.6. Their differentiated positioning is now clear: GLM‑5.1 excels in code and long‑task engineering, Qwen Plus leads in cost‑effective multimodal capability, and MiniMax M2.7 offers the most balanced, affordable all‑round solution. The best choice depends entirely on the specific scenario.