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Cognition releases the coding model SWE-2 Flash. According to the Cognition model/Devin blog, the model is trained on Thinking Machines' Inkling-Small (276B total parameters, 12B activated parameters), and in the training pipeline a mid-training stage before reinforcement learning was added for the first time. The company claims Terminal-Bench 4.0 improved from 0.9% to 20.0%, FrontierCode 2.0 from 3.2% to 24.0%, and ExploitBench from 11.3% to 34.8%. The model is now available on Devin Desktop and CLI, priced at one-fifth of the larger SWE-2 from the same family, and three times faster (200 tok/s versus 60 tok/s); its weights are not yet public, with plans to share them securely with trusted industry partners and research institutions through a program. Cognition also claims that the model surpasses all US open-weight models on major benchmarks, including models with 3 times as many parameters; within this month, Pro, Max and Teams users can use it for free in Devin Desktop and Devin CLI.
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Cognition Releases SWE-2 Flash Coding Model, Three Times Faster and One-Fifth the Price
Cognition released its coding model SWE-2 Flash on November 4, 2026, saying it runs three times faster than the larger SWE-2 and costs one-fifth as much. It is available in Devin Desktop and the CLI and is free to use until November 30, 2026. The model has 276 billion total parameters and 12 billion active parameters, is adapted from Thinking Machines Lab's Inkling-Small mixture-of-experts model, and targets routine tasks such as automation, code review, feature development, and documentation updates. On benchmarks, Flash is slightly higher than SWE-2 on FrontierCode 2.0, but lower than SWE-2 on Terminal-Bench 4.0 and DeepSWE 1.1; its weights are not yet public, and Cognition is setting up a secure sharing program for trusted partners and research institutions.
Soumith ChintalaRelated discussion
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CognitionFirst-partySelected
Cognition Releases SWE-2 Flash Coding Model
Cognition has released SWE-2 Flash, a fast and efficient coding model, claiming it outperforms all US open-weight models on major benchmarks, including models 3 times its size. The model is based on thinkymachines' Inkling-Small, with 276B total parameters and 12B activated parameters, and is 5 times cheaper and 3 times faster than SWE-2. Within this month, Pro, Max, and Teams users can use it for free in Devin Desktop and Devin CLI, and Cognition also plans to launch a partnership program for securely sharing weights.
CognitionFirst-party
Cognition Releases SWE-2 Flash Coding Model, Free in Devin This Month
Cognition has released SWE-2 Flash, a fast and efficient coding model, free this month for Pro, Max, and Teams users of Devin Desktop and Devin CLI. The model is based on thinkymachines' Inkling-Small, with 276B total parameters and 12B activated parameters; combined with an optimized inference stack, it costs 5 times less and runs 3 times faster than SWE-2. Cognition says it outperforms all US open-weight models on major benchmarks, and is building a partnership program for securely sharing weights.
CognitionFirst-partySelected
Cognition Releases SWE-2 Flash Coding Model and Discloses Training Details
Cognition releases the fast coding model SWE-2 Flash, based on thinkymachines' Inkling-Small (276B total parameters, 12B activated parameters), at 5x lower cost and 3x faster than SWE-2. Training uses large-scale RL, with adjustments made to the SWE-2 recipe, but the main gains come from higher-quality, more diverse RL tasks; the research team audited task instructions, environment configurations, and verifiers and added several new task types. The model also adds a mid-training stage before RL for the first time, trained on trillions of tokens of code, scientific articles, and blogs, and is available free of charge to Pro, Max, and Teams users in Devin Desktop and Devin CLI until the end of this month.
CognitionFirst-partySelected
Cognition Releases SWE-2 Flash Coding Model, Built on Inkling-Small with New…
Cognition has released the fast coding model SWE-2 Flash, built on thinkymachines' Inkling-Small (276B total parameters, 12B active parameters), at 5x lower cost and 3x faster than SWE-2. The model adds a mid-training stage for the first time before RL, training on trillions of tokens of code, scientific articles, and blogs, saving the scarcest high-quality data for last. Cognition says it surpasses all US open-weight models on major benchmarks, including models with 3x more parameters, and plans to safely share the weights with partners and researchers through the program; Pro, Max, and Teams users of Devin Desktop and Devin CLI can use it for free until the end of this month.
CognitionFirst-partySelected
Cognition Releases Coding Model SWE-2 Flash, Built on Inkling-Small
Cognition has released the fast coding model SWE-2 Flash, built on thinkymachines' Inkling-Small, with 276B total parameters and 12B activated parameters. The company claims it costs 5 times less and is 3 times faster than SWE-2, and surpasses all US open-weight models on major benchmarks, including models 3 times larger. The model will be free this month for Pro, Max, and Teams users of Devin Desktop and Devin CLI, and its weights will be released on a targeted basis through partner programs.
CognitionFirst-partySelected
Cognition Releases Fast Coding Model SWE-2 Flash
Cognition released the fast coding model SWE-2 Flash, claiming it surpasses all US open-weight models on major benchmarks, including models with 3 times the parameter count. The model is based on thinkymachines' Inkling-Small (276B total parameters, 12B active), paired with an optimized inference stack, and is 5 times cheaper and 3 times faster than SWE-2. Within this month, Pro, Max, and Teams users can use it for free in Devin Desktop and Devin CLI, and the weights will be opened through a secure sharing program with partners and researchers.
Cognition Model / Devin BlogFirst-partySelected
Cognition Releases SWE-2 Flash Coding Model
Cognition has released the SWE-2 Flash coding model, trained on Thinking Machines' Inkling-Small (276B total parameters, 12B activated parameters), marking the first time a mid-training stage before reinforcement learning was added to the training pipeline. The company claims Terminal-Bench 4.0 improved from 0.9% to 20.0%, FrontierCode 2.0 from 3.2% to 24.0%, and ExploitBench from 11.3% to 34.8%. The model is now available on Devin Desktop and the CLI, priced at one-fifth of its larger sibling SWE-2 and running 3 times faster (200 tok/s versus 60 tok/s). Its weights are not yet public; the company plans to share them securely with trusted industry partners and research institutions through a program.
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