Claude Opus 5 is now available in the Kiro IDE, CLI, and Web. It's a meaningful step up from Opus 4.8 on the hardest agentic coding tasks — setting new state-of-the-art results on leading coding benchmarks, more than doubling Opus 4.8's performance while approaching Fable 5 at half the cost. Excels at multi-file features, large refactors, and end-to-end feature work, completing full tasks rather than leaving stubs or placeholders. Opus 5 verifies its own work and iterates carefully until it succeeds. Multi-agent coordination sees meaningful gains with fewer conflicts between parallel agents. For code review, it finds a high rate of real bugs with low false positives and stays accurate at lower effort settings. Claude Opus 5 is rolling out gradually with experimental support to Kiro Pro, Pro+, Pro Max, and Power customers in us-east-1 and eu-central-1 with cross-region inference. 1M context window. 2.2x credit multiplier. Restart your IDE or CLI to access it from the model selector. Learn more ->
OpenAI models are available in Kiro for the first time. GPT-5.6 Sol, Terra, and Luna bring three tiers of frontier intelligence to the IDE, CLI, and Web — each tuned for a different point on the performance-cost curve. Sol is the flagship, built for the hardest multi-step work: spec-driven implementation, long-horizon refactors, and complex terminal tasks. It scores 80 on the Coding Agent Index and 88.8% on Terminal-Bench 2.1. Terra is the balanced option for routine multi-step development at a fraction of flagship cost. Luna is the fastest, lowest-cost tier for high-frequency tasks where throughput matters most. All three run longer without supervision and can write lightweight programs that coordinate tools and process intermediate results, reducing round-trips and dead ends. Hidden chain-of-thought means you see final output only. 272K context window across all tiers. Kiro credit multipliers: Sol 2.4x, Terra 1.2x, Luna 0.6x. Experimental support is rolling out to Pro, Pro+, Pro Max, and Power customers in us-east-1 and eu-central-1 with cross-region inference. Learn more ->
Claude Sonnet 5 is now available in the Kiro IDE, CLI, and Web. It's Anthropic's most agentic Sonnet model yet, a substantial upgrade from Sonnet 4.6 with stronger reasoning, tool use, and coding that approaches Opus 4.8 performance at Sonnet-class pricing. Plans before it edits, runs longer without supervision, and checks its own output without being asked. Sonnet 5 is rolling out gradually with experimental support to Kiro Pro, Pro+, Pro Max, and Power customers in the AWS US-East-1 (Northern Virginia) and AWS Europe (Frankfurt) regions, with cross-region inference support. It ships with the full 1M context window, 1.3x credit multiplier, the same as Sonnet 4.6. Restart your IDE or CLI to access it from the model selector. Learn more ->
Claude Opus 4.8 is now available in the Kiro IDE, CLI, and Web. A direct upgrade from Opus 4.7 with stronger self-verification, more efficient tool calling, and better follow-through on long-horizon projects. Rolling out with experimental support to Pro, Pro+, and Power customers. Ships with a 1M context window, 128K max output, and a 2.2x credit multiplier. Inference runs in us-east-1 (N. Virginia) and eu-central-1 (Frankfurt). Restart your IDE or CLI to access it from the model selector. For the Kiro CLI users to be able to get the best experience out of Opus 4.8, they need to be on Kiro CLI v2.5.0 or above. Learn more ->
Claude Opus 4.7 is now available for Pro, Pro+, and Power subscribers, and IAM Identity Center users, in the Kiro IDE and CLI. Opus 4.7 features adaptive thinking, which automatically adjusts how much reasoning the model uses based on task complexity, spending more time on harder problems and responding quickly to simpler ones. For the best performance and efficiency, you need to be on the latest version of the IDE (0.11.133) and CLI (2.2.0) or later. Learn more ->
Added support for Claude Opus 4.7, Anthropic's latest Opus model with stronger agentic coding performance, more precise instruction following, and 3x higher resolution vision. Rolling out with experimental support to a subset of Pro, Pro+, and Power tier subscribers signing in with AWS IAM Identity Center, with broader availability to follow. Support for Google, GitHub, and AWS Builder ID sign-in is coming soon. 1M token context window with a 2.2x credit multiplier. Inference runs in us-east-1 (N. Virginia) and eu-central-1 (Frankfurt). Restart your IDE or CLI to access it from the model selector. Learn more ->
Added support for GLM-5, a sparse mixture-of-experts model with a 200K context window designed for complex systems engineering and long-horizon agentic tasks. GLM-5 excels at processing repository-scale context and maintaining coherence during multi-step tool use across large codebases, making it a strong choice for cross-file migrations, full-stack feature development, and legacy refactoring where the model needs to hold the full picture. Available with experimental support in both the Kiro IDE and Kiro CLI. Inference runs in us-east-1 (N. Virginia) with a 0.5x credit multiplier. Restart your IDE or CLI to access it from the model selector. Learn more ->
Claude Opus 4.6 and Claude Sonnet 4.6 now support a 1M context window, up from 200K, and are no longer marked as experimental. Both models are now generally available in the Kiro IDE and Kiro CLI for Pro, Pro+, and Power tier subscribers.
Restart your IDE or CLI to see the updated models. Learn more ->
MiniMax M2.5 is now available in eu-central-1 (Frankfurt) for users authenticating with AWS IAM Identity Center, extending support beyond us-east-1 (N. Virginia).
Available on all subscription tiers with a 0.25x credit multiplier and 200K context window. Restart your IDE or CLI to access it from the model selector. Learn more ->
A new open weight model is now available with experimental support in the Kiro IDE and CLI. Available on Free, Pro, Pro+, and Power tiers with all authentication methods. Supported in us-east-1 (N. Virginia). Restart your IDE or CLI to access it from the model selector.
MiniMax M2.5 matches frontier-class coding performance at a fraction of the cost. Trained with reinforcement learning across hundreds of thousands of real-world environments, it delivers strong results across the full development lifecycle from system design to code review and completes complex agentic tasks significantly faster than its predecessor. 200K context window with a 0.25x credit multiplier.