# 1M-token context window

Z.ai released GLM 5.2, a flagship built for long-horizon agentic tasks on a solid 1M-token context and positioned by its published benchmarks as the strongest open-source coding model. Rather than forwarding vendor numbers, Fireworks ran the open weights on its own GPUs and inference engine and scored 91.4% (181/198) on GPQA-Diamond in high reasoning mode, matching Z.ai's reported 91.2%. The model ships under an MIT license and is live on Fireworks for developers and LLM researchers.

Z.ai released GLM 5.2, a flagship built for long-horizon agentic tasks on a solid 1M-token context and positioned by its published benchmarks as the strongest open-source coding model. Rather than forwarding vendor numbers, Fireworks ran the open weights on its own GPUs and inference engine and scored 91.4% (181/198) on GPQA-Diamond in high reasoning mode, matching Z.ai's reported 91.2%. The model ships under an MIT license and is live on Fireworks for developers and LLM researchers.

Meta has released the weights for Muse Spark Open, a model built for agentic coding and long-horizon tasks, and it is now served on Modal as a Shared Endpoint with per-token billing. It offers native multimodal understanding and a 1M-token context window, covering code writing and debugging, multi-step planning, sequential tool use and failure recovery across extended workflows. In Meta's evaluations it scores 76.4 on Terminal-Bench 2.1 with the Terminus 2 agent and 60 on DeepSWE 1.1 with Meta's preferred harness at xhigh reasoning effort.

Meta has released the weights for Muse Spark Open, a model built for agentic coding and long-horizon tasks, and it is now served on Modal as a Shared Endpoint with per-token billing. It offers native multimodal understanding and a 1M-token context window, covering code writing and debugging, multi-step planning, sequential tool use and failure recovery across extended workflows. In Meta's evaluations it scores 76.4 on Terminal-Bench 2.1 with the Terminus 2 agent and 60 on DeepSWE 1.1 with Meta's preferred harness at xhigh reasoning effort.

MiniMax's open-weight multimodal M3.1 Flash is now free on AI Gateway through Oct 22, and only when MiniMax serves the request. The model targets coding and agentic work with a 1M-token context window for long documents and multi-step agent sessions. Compared with earlier MiniMax models, MiniMax says it improves on complex agentic tasks, coding and cybersecurity, with its best coding results on application development, particularly frontend interactions, targeted bug fixes and component tests.

MiniMax's open-weight multimodal M3.1 Flash is now free on AI Gateway through Oct 22, and only when MiniMax serves the request. The model targets coding and agentic work with a 1M-token context window for long documents and multi-step agent sessions. Compared with earlier MiniMax models, MiniMax says it improves on complex agentic tasks, coding and cybersecurity, with its best coding results on application development, particularly frontend interactions, targeted bug fixes and component tests.

Reflection AI's Beam reasoning model has left stealth testing under the codename stealth/cat-jam and is now available on AI Gateway under its own name, free for a limited launch period when served by Reflection AI. It targets coding and agentic tasks, ships weights under Apache 2.0, supports tool calling and structured outputs, and carries a 1M-token context window. The model has 501B total parameters but activates only 23B per token and skips tokens a task does not need, so it tends to answer sooner than larger open models.

Reflection AI's Beam reasoning model has left stealth testing under the codename stealth/cat-jam and is now available on AI Gateway under its own name, free for a limited launch period when served by Reflection AI. It targets coding and agentic tasks, ships weights under Apache 2.0, supports tool calling and structured outputs, and carries a 1M-token context window. The model has 501B total parameters but activates only 23B per token and skips tokens a task does not need, so it tends to answer sooner than larger open models.