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Tinker@tinkerapiOfficialAI score31AITinker says it made efficiency improvements to support scaling long-context reinforcement learning and is passing them on through price cuts of up to 70%. GLM-5.3-Flash and DeepSeek-v4.1-Flash are now live on the platform for cost-efficient long-context work.
Tinker@tinkerapiOfficialAI score40AITinker adds GLM-5.3-Flash and DeepSeek-v4.1-Flash, both of which natively accept image inputs and use efficient attention architecture. GLM-5.3-Flash costs 4-5 times less on Tinker than GLM-5.3. Long-context options for Qwen3.5-4B and Qwen3.6-35B-A3B are also live.
AIDeepSeek released open weights for V4.1-Flash, which Baseten now offers through its Model APIs. The model has 552B total parameters, 8B active for prefill and 16B for decode, a 1M token context window, and text plus image input. Its Causal Encoder-Decoder design runs only the encoder during prefill and reuses a projected KV cache, and the source reports the global KV cache at a quarter of V4-Flash's memory.
Why it matters: The post explains how the CED architecture splits prefill and decode compute and cuts KV cache memory, which matters for coding agent costs.
DeepSeek@deepseek_aiOfficialPickAI score72AIDeepSeek says V4.1-Flash is now live on its API with native multimodal support, accessed through the model name deepseek-flash. The older V4-Flash and V4-Flash-Vision-Exp are retired, while deepseek-v4-flash and deepseek-v4-flash-vision-exp temporarily route to V4.1-Flash. Requests to deepseek-v4-pro will route to V4.1-Flash at V4.1-Flash rates starting 04:00 UTC on Sept 14, 2026, until V4.1-Pro launches.
AIDeepSeek officially released DeepSeek-V4.1-Flash, the smallest model in its new architecture family, with native multimodal visual understanding. The API now serves it under the model name deepseek-flash, while V4 Flash and V4 Flash Vision Exp were retired and routed to V4.1 Flash. API prices were reduced with the release, and V4 Pro remains available after September 14, 2026.
Why it matters: The release lists benchmark results alongside API model-name changes and retirements, so developers can check both capability claims and migration steps.
AIDeepSeek released DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts model with 552B backbone parameters and support for contexts up to one million tokens. The technical report says its global KV cache footprint is 890 bytes per token, roughly one quarter of DeepSeek-V4-Flash, and reports 8B activated parameters per token during prefill and 16B during decode.
Why it matters: The report shows KV cache per token falling to about one quarter of DeepSeek-V4-Flash, a concrete tradeoff between long-context serving cost and benchmark results.