Together AI said GLM-5.3 now beats GPT-5.6 Sol and Claude Fable 5 on agentic benchmarks, with GLM-5.3 Flash close behind. Zhipu kept the GLM-5.2 base and scaled post-training—more long-horizon environments, more diverse tasks, and more RL compute—rather than training a new foundation model. For coding agents, that is a claim that post-training stack height can overtake closed flagships, and that the cheaper open-weight default is already on Together’s API.
Key Takeaways
- ✓Together reports GLM-5.3 ahead of GPT-5.6 Sol and Claude Fable 5 on agentic benchmarks; Flash is next.
- ✓The gain is post-training on the GLM-5.2 base: more long-horizon RL, not a new pretrained model.
- ✓Coding agents can switch to a cheaper open-weight default without waiting on a new foundation run.
⚡
TL;DR VerdictKey Decision Metrics at a Glance
🧭
Turn your technical choice into a development budget
Compare 40 dev plans & simulate token costs vs $20/mo subscriptions
Action HubReady to adopt this in production?
Benchmark side-by-side against alternatives, or calculate monthly token cost vs subscription break-even.
Up NextCline: GLM-5.3 max outperforms GPT-5.6 Sol max on Terminal-Bench 4.0
Read Now→
Discussion & Comments
0Sign in to join the discussion
Connect with AI developers to exchange benchmark insights.