GLM / Z.ai (Zhipu AI) vs AI21 Jamba2

Which one should you pick? Here's the full breakdown.

Our Pick

GLM / Z.ai (Zhipu AI)

A
8.0/10

Zhipu AI's open-weights family -- GLM-5.1 (launched 2026-04-07) is 744B MoE / 40B active, topped SWE-Bench Pro at 58.4 (beating GPT-5.4 and Claude Opus 4.6), MIT licensed, 200K context. Trained entirely on 100K Huawei Ascend 910B chips -- first frontier model with zero Nvidia in the training stack

AI21 Jamba2

A
8.0/10

AI21 Labs' hybrid SSM-Transformer (Mamba-style) open-weight family -- Jamba2 launched 2026-01-08. Two sizes: 3B dense (runs on phones / laptops) and Jamba2 Mini MoE (12B active / 52B total). Apache 2.0, 256K context, mid-trained on 500B tokens

CategoryGLM / Z.ai (Zhipu AI)AI21 Jamba2
Ease of Use6.56.5
Output Quality8.58.0
Value9.09.0
Features8.08.5
Overall8.08.0

Pricing Comparison

FeatureGLM / Z.ai (Zhipu AI)AI21 Jamba2
Free TierYesYes
Starting Price$0$0

Benchmark Head-to-Head

GLM-5.1 (744B MoE / 40B active) benchmarks — AI21 Jamba2 has no published benchmarks

BenchmarkScore
SWE-Bench Pro58.4%
MMLU-Pro81.2%
GPQA Diamond74.5%
HumanEval89.1%
SWE-Bench Verified64.2%
BFCL (function calling)88%

Which Should You Pick?

Pick GLM / Z.ai (Zhipu AI) if...

Teams that need genuine MIT-licensed frontier open weights with no commercial strings. Especially strong for agentic workflows and vision (GLM-4.6V).

Visit GLM / Z.ai (Zhipu AI)

Pick AI21 Jamba2 if...

Developers building long-context RAG systems (256K context with manageable memory is the sweet spot), mobile/edge deployments where Jamba2 3B's hybrid efficiency shines, and teams that want to experiment with non-transformer architectures while staying in Apache-2.0 territory. Also good for Israeli + EU enterprise procurement where AI21's geography / GDPR posture matters.

Visit AI21 Jamba2

Our Verdict

GLM / Z.ai (Zhipu AI) and AI21 Jamba2 are extremely close overall. Your choice comes down to specific needs -- GLM / Z.ai (Zhipu AI) is better for teams that need genuine mit-licensed frontier open weights with no commercial strings, while AI21 Jamba2 works best for developers building long-context rag systems (256k context with manageable memory is the sweet spot), mobile/edge deployments where jamba2 3b's hybrid efficiency shines, and teams that want to experiment with non-transformer architectures while staying in apache-2.