VentureBeat Mar 19, 08:31 PM
Cursor’s new coding model Composer 2 is here: It beats Claude Opus 4.6 but still trails GPT-5.4 Cursor, a San Francisco AI coding platform from startup Anysphere valued at $29.3 billion, has launched Composer 2, a new in-house coding model now available inside its agentic AI coding environment, and it offers drastically improved benchmarks from its prior in-house model.
It's also launching and making Composer 2 Fast, a higher-priced but faster variant, the default experience for users.
Here's the cost breakdown:
Composer 2 Standard: $0.50/$2.50 per 1 million input/output tokens
Composer 2 Fast: at $1.50/$7.50 per 1 million input/output tokens
That's a big drop from Cursor's predecessor in-house model, Composer 1.5, from February, which cost $3.50 per million input tokens and $17.50 per million output tokens; Composer 2 is about 86% cheaper on both counts.
Composer 2 Fast is also roughly 57% cheaper than Composer 1.5.
There's also discounts for "cache-read pricing," that is, sending some of the same tokens in a prompt to the model again, of $0.20 per million tokens for Composer 2 and $0.35 per million for Composer 2 Fast, versus $0.35 per million for Composer 1.5.
It also matters that this appears to be a Cursor-native release, not a broadly distributed standalone model. In the company’s announcement and model documentation, Composer 2 is described as available in Cursor, tuned for Cursor’s agent workflow and integrated with the product’s tool stack.
The materials provided do not indicate separate availability through external model platforms or as a general-purpose API outside the Cursor environment.
Cursor is pitching long-horizon coding, not just better completions
The deeper technical claim in this release is not merely that Composer 2 scores higher than Composer 1.5. It is that Cursor says the model is better suited to long-horizon agentic coding.
In its blog, Cursor says the quality gains come from its first continued pretraining run, which gave it a stronger base for scaled reinforcement learning. From there, the company says it trained Composer 2 on long-horizon coding tasks and that the model can solve problems requiring hundreds of actions.
That framing is important because it addresses one of the biggest unresolved issues in coding AI. Many models are good at isolated code generation. Far fewer remain reliable across a longer workflow that includes reading a repository, deciding what to change, editing multiple files, running commands, interpreting failures and continuing toward a goal.
Cursor’s documentation reinforces that this is the use case it cares about. It describes Composer 2 as an agentic model with a 200,000-token context window, tuned for tool use, file edits and terminal operations inside Cursor.
It also notes training techniques such as self-summarization for long-running tasks. For developers already using Cursor as their main environment, that tighter tuning may matter more than a generic leaderboard claim.
The benchmark gains are substantial, even if GPT-5.4 still leads on one key chart
Cursor’s published