Bito

New coding models keep changing the equation for engineering teams. We put Claude Sonnet 5.5 and Opus 5.5 through the same set of real coding tasks to measure what you get for the extra cost. Sonnet 5.5 comes close to Opus on many tasks, at a fraction of the cost. Opus still pulls ahead on the work that needs judgment: reasoning about failure modes, reviewing designs, and pushing back on a plan rather than building it. We break down the results, task by task, looking at both quality and cost. Full comparison: https://lnkd.in/gtkwUF37

Another takeaway from our cost-quality frontier research: Of the 29 models we tested, 21 have a cheaper alternative that performs just as well or better. Claude Opus 5: 54/60 for $135.51 DeepSeek V4.1 Flash: 50/60 for $2.67 93% of the top score for 2% of the cost. 7 more models come close to the leader, within 4.5 points, and cost as little as $2.58. Only 8/29 models sit on the efficient frontier, where nothing else is both cheaper and better. For engineering teams running agents at scale, the right model balances performance and cost. Full board and methodology in the comments.

We ran ~30 models on 1,500+ real engineering tasks and graded and priced the answers. One of the largest engineering runs, studying the pricing-quality frontier. Three things stood out: 1. The rate card no longer predicts the bill. Claude Fable 5.1 lists at 2x Claude Opus 5 per token and costs 37% less to run.  2. Price moves far faster than score. claude-opus-5 scored 54.5, cost $135.51. deepseek-v4.1-flash scored 50, cost $2.67. 93% of the score, 2% of the cost.   3. Most models guess when a request is vague. And confuse the user. Buying the newest flagship used to be a safe default. Our data says that era is over. Full board and method, live now. Link in comments.

We ran a normal ten turn session through a coding agent. Trace a Slack event through Kafka. Find where OAuth lives. Check whether a Jira integration already exists. The session cost $6.81. $5.30 of that went to the agent grepping and reading files before it could do anything with them. The compounding explains it. Each step sends everything gathered so far back to the model, so a file read on turn two still rides along on turn twenty. Every token the agent gathered went back through the model about 50 times. We reran the same ten prompts with Bito's AI Architect connected. AI Architect keeps a live index of every repo, resolves each question to the relevant code, and hands the agent that span before it starts hunting. Same model, same caching. With nothing left to search for, the agent read 3 files instead of 62 and pushed 1.4 million tokens through the model instead of 5.3 million. $1.13. 8.5 minutes instead of 25. Five runs per arm, and it held every time. AI Architect reaches your agents through Governor, the layer that sits between your coding agents and your models. One environment variable, and nothing else changes. Full breakdown here: https://lnkd.in/gt8kwQrk

Model prices keep falling, and agent bills keep climbing anyway. Watch a session run and you see why. The agent greps the codebase, re-reads its own transcript on every step, and spends most of its budget locating where the change belongs rather than making it. Bito's Governor removes that search. It sits between your coding agents and your models, attaches a map of the relevant files and dependencies to each request, then routes that request to a model sized for the work involved. On a customer A/B, same tasks and same harness, cost per task fell from $4.12 to $2.14 while success held at 100%. Link in comments.

Everything your team does in the IDE now runs from a Slack thread. → Sprint standup brief on demand → Feasibility and impact on a PRD → Grounded implementation plan → Merge request from a thread → Production issue triage Bito's AI Architect carries the same system context in Slack that grounds coding agents in Cursor, Claude Code, Codex, or any MCP client. 10 ways teams are using it, below:

Autonomous agents are now in closed beta 🎉 Give them a spec. A fleet works every repo the change touches, all the way to a reviewed pull request. Migrations, cross-service features, refactors, tech debt. Your engineers review at every stage. More here: https://lnkd.in/dNeSwpxQ

Bito's AI Architect now reads your Google Docs. Engineering teams keep PRDs, design decisions, and technical specs spread across Google Docs. That context has been invisible to coding agents and code reviews until now. AI Architect now pulls from Google Docs the same way it already pulls from Confluence, Jira, Linear, Slack, and your codebase. One more surface feeding the same knowledge graph. Connect your Google account at alpha.bito.ai.