AI Research

SWE-1.5 model ends the AI speed vs. smarts tradeoff

The race for the best AI coding assistant has often forced a difficult choice on developers: do you want a model that’s fast, or one that’s smart?

StartupHub.ai Staff

By StartupHub.ai Staff, Editorial team

2 min read

swe-1.5 model ends the ai speed vs. smarts tradeoff vs vs  comparison

The race for the best AI coding assistant has often forced a difficult choice on developers: do you want a model that’s fast, or one that’s smart? A new company, spun out of the team behind the Devin agent, claims its new SWE-1.5 model finally eliminates that tradeoff. In a blog post, the team announced that SWE-1.5, a "frontier-size model with hundreds of billions of parameters," achieves near state-of-the-art coding performance while setting a new benchmark for speed.

Partnering with inference provider Cerebras, the company says it can serve the SWE-1.5 model at up to 950 tokens per second. To put that in perspective, they claim it's 13 times faster than Anthropic's Sonnet 4.5 and 6 times faster than Haiku 4.5. For a developer, that’s the difference between an AI completing a task in 20 seconds versus under five, a crucial margin that keeps them in the "flow state."

More than just a model

The secret sauce, according to the team, isn’t just a better model but a reimagined stack. They argue that the model, the "agent harness" that orchestrates its actions, and the inference engine must be co-designed as a single, unified system. "Working on Devin, we often wished we could co-develop the model and the harness, and with this release we are finally able to do just that," the announcement states.

This holistic approach involved end-to-end reinforcement learning using a custom agent harness, rewriting core tools to eliminate bottlenecks, and training on NVIDIA's new GB200 NVL72 chips, making SWE-1.5 potentially the first public production model trained on the new hardware.

The team also took a novel approach to training data, moving beyond standard benchmarks like SWE-Bench, which they say have a "narrow task distribution." Instead, they created their own high-fidelity coding environments and evaluation rubrics designed by senior engineers to combat "AI slop", code that works but is verbose or uses anti-patterns. This focus on real-world tasks and code quality, combined with the raw speed of the underlying system, is what they believe sets SWE-1.5 apart.

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While the company has stopped reporting SWE-Bench scores, it claims SWE-1.5 achieves "near-frontier performance" on the more advanced SWE-Bench Pro benchmark. The SWE-1.5 model is now available in the company's Windsurf product.

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Topics/dev/agentsAIAI AgentsCerebrasLarge Language Models (LLMs)LaunchNvidiaPartnership

StartupHub.ai Staff

Written by

StartupHub.ai Staff

Editorial team

The staff writers of StartupHub.ai, ranging from investment analysts to avid AI tool users, early adopters and critical enthusiasts. Backgrounds span engineering, business and the arts. We hold every piece to rigorous standards of research and review.

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