Smart Stack

Side-by-side pricing, features and compliance for any tools in the directory.

GLM-5.2 vs Qwen3.8-Max

GLM-5.2

Z.ai

Pick it if: GLM-5.2 is Z.ai's flagship released June 13, 2026 on a 744B MoE architecture with 40B active parameters and a 1,000,000-token context window. Priced at $1.40/$4.40 per 1M tokens under an MIT license, it scores 80.3% GPQA Diamond and 62.1% SWE-bench Pro.

Its edge: Tops open-source SWE-bench Pro at 62.1% as of June 2026, 3.7 points ahead of predecessor GLM-5.1.

The catch: No native image, audio, or video input — vision tasks require the separate GLM-5V-Turbo model.

Qwen3.8-Max

Alibaba Cloud

Pick it if: Qwen3.8-Max launched GA on August 3, 2026 with 2.4 trillion total parameters (about 95 billion active) and a 1-million-token context window across a single flat pricing tier. It targets teams building large-context, multimodal agentic workloads on Alibaba Cloud who can tolerate its still-unpublished safety and training model card.

Its edge: Leads PaperBench at 93.0, ahead of GPT-5.6 Sol (90.5), Fable 5 (88.8) and Opus 4.8 (80.3).

The catch: No published safety or training model card as of the 2026-08-03 GA launch.

Pricing

Input / 1M tokens$1.40$2
Output / 1M tokens$4.40$6
Cached input / 1M$0.26
Blended cost (3:1)$2.15$3
Free tierfalsetrue
Pricing modelper-tokenper-token

Verdict & fit

StrengthsTops open-source SWE-bench Pro at 62.1% as of June 2026, 3.7 points ahead of predecessor GLM-5.1.; 1-million-token context window at $1.40/1M input — the largest context available in any MIT-licensed model.; MIT license and OpenAI-compatiblLeads PaperBench at 93.0, ahead of GPT-5.6 Sol (90.5), Fable 5 (88.8) and Opus 4.8 (80.3).; 1-million-token context window (991.8K input non-thinking / 983.6K thinking) with a single flat pricing tier covering the full window.; $2.00 / $6.0
LimitationsNo native image, audio, or video input — vision tasks require the separate GLM-5V-Turbo model.; No disclosed SOC 2, HIPAA, or ISO 27001 certifications, limiting adoption in regulated industries.; 1M-context recall quality at depth has no puNo published safety or training model card as of the 2026-08-03 GA launch.; Trails Fable 5 on Humanity's Last Exam (43.6 vs 53.3) and SWE-bench Pro (67.7 vs 80.0).; Open weights promised 'next week' at launch but not yet shipped; API-only f

Capability envelope

Context window1M1M
Max output tokens131.1K131.1K
Input modalitiestext; tool-callstext; image; video
Output modalitiestext; tool-callstext
CapabilitiesTool use; Function calling; Structured outputVision; Tool use; Video input
Reasoning modesstandard; extended-thinking-high; extended-thinking-maxstandard; thinking (preserved reasoning)
Long-context recallmediumunverified
Opennessopen-sourceproprietary

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