A 4K AI image costs 5 credits on Callirra, or $0.05, versus $0.11–$0.40 elsewhere

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Callirra prices a 4K image at 5 credits ($0.05) on Nano Banana 2.1 and 8 to 35 credits on GPT Image 2.5, depending on quality setting. Google's published image-output equivalent for Nano Banana 2.1 at 4K was about $0.113 when last checked, while Runway and fal list 4K Nano Banana Pro at $0.40 and $0.30 respectively. The author notes these are not like-for-like products, but says the difference in order of magnitude matters for workflow decisions. On Callirra, Nano Banana 2.1 steps from 3 credits at 1K to 4 at 2K to 5 at 4K, so drafting at 1K and finishing at 4K costs little extra. Prices change over time, so the author advises rechecking current rates before quoting a client.

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Cognition launches SWE-2 Flash, a faster, cheaper coding model for Devin users

Cognition, maker of the Devin coding agent, has released SWE-2 Flash, a smaller companion to its flagship SWE-2 model. The company says it runs about three times faster at roughly one-fifth the price, aimed at routine tasks such as automations, code review, feature work and documentation. The model is a 276-billion-parameter mixture-of-experts design with 12 billion parameters active per token, built on Thinking Machines Lab's Inkling-Small base. It is available in Devin Desktop and the Devin CLI, free through November 30, 2026, but has no standalone API or public weights. Cognition's own benchmarks are mixed relative to SWE-2, with Flash ahead on one test and behind on two others, and its speed comparison is a simulation rather than a measured result.

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ProgrammingDEV Community ·

Liquid AI's LFM3 Small Model Outperforms Larger Rivals on Agent Tasks

Liquid AI has released LFM3, a new family of small open-weight models, on October 27, 2026. Its flagship LFM3-1.7B fits in roughly 1 GB at 4-bit precision and supports a 256,000-token context window. The model uses a hybrid design mixing convolution layers with sliding-window and global attention, an architecture selected through hardware-in-the-loop testing across phones, laptops, CPUs, GPUs, and an NPU. On agent-oriented benchmarks such as WebArena-Verified, AutomationBench, and function calling, it beats the much larger Qwen 3.5 9B. It trails bigger models on agentic coding and knowledge-heavy tests like SWE-bench and GPQA, which Liquid acknowledges as expected for a model of its size.

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ProgrammingDEV Community ·

Schema FAQPage và HowTo: viết JSON-LD khớp HTML để AI Overviews trích đúng

Many teams add a JSON-LD block for FAQ schema and assume that completes their AI Overviews optimization, but a passing Rich Results Test can still hide mismatches between the markup and the visible page. The article argues that schema does not replace indexing or the presence of content in the raw HTML, since AI bots often ignore anything injected by JavaScript. FAQPage suits pages with real customer questions where each answer stands alone, while HowTo fits ordered processes like implementation steps, and the schema text must match the on-page wording nearly word for word. Common errors include marketing-style FAQs that visitors never ask, answers in the markup that differ from the visible copy, and duplicated or copied schema left unedited. The author recommends a quick curl check to confirm the JSON-LD appears in the initial HTML before deployment.

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ProgrammingDEV Community ·

B300 Offers 60% More Memory per GPU Than B200, But Not Faster Training Compute

NVIDIA's Blackwell Ultra B300 carries 288 GB of memory per GPU versus 180 GB on the B200, while FP8 throughput, memory bandwidth and NVLink bandwidth remain unchanged. A cloud pricing comparison lists the B300 at $6.943 per hour and the B200 at $5.50 per hour. Because of this gap, a B300 would need to finish a training job about 1.26 times faster to be the cheaper option. The analysis concludes the B300 mainly makes sense when its extra memory avoids adding a node or offloading to the CPU, or when training uses NVFP4. Its other improvements, such as faster FP4 and attention math, are seen as more relevant to inference than to typical training workloads.

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