Anthropic released Claude Haiku 4.5, a latency-optimized “small” model that delivers similar levels of coding performance to Claude Sonnet 4 while running more than twice as fast at one-third the cost. The model is immediately available via Anthropic’s API and in partner catalogs on Amazon Bedrock and Google Cloud Vertex AI. Pricing is $1/MTok input and $5/MTok output. Anthropic positions Haiku 4.5 as a drop-in replacement for Haiku 3.5 and Sonnet 4 in cost-sensitive, interactive workloads.
Positioning and lineup
Haiku 4.5 targets real-time assistants, customer-support automations, and pair-programming where tight latency budgets and throughput dominate. It surpasses Sonnet 4 on “computer use” tasks—the GUI/browser manipulation underpinning products like Claude for Chrome—and is described as materially improving responsiveness in Claude Code for multi-agent projects and rapid prototyping. Anthropic makes clear that Sonnet 4.5 remains the frontier model and “the best coding model in the world,” while Haiku 4.5 offers near-frontier performance with greater cost-efficiency. A recommended pattern is Sonnet 4.5 for multi-step planning and parallel execution by a pool of Haiku 4.5 workers.
Availability, identifiers, and pricing
From day one, developers can call the model (claude-haiku-4-5) on Anthropic’s API. Anthropic also states availability on Amazon Bedrock and Vertex AI; model catalogs may update region coverage and IDs over time, but the company confirms cloud availability in the launch post. The API price for Haiku 4.5 is $1/MTok (input) and $5/MTok (output), with prompt-caching listed at $1.25/MTok write and $0.10/MTok read.
Benchmarks
Anthropic summarizes results across standard and agentic suites and includes methodology details to qualify the numbers:
- SWE-bench Verified: simple scaffold with two tools (bash, file edits), 73.3% averaged over 50 trials, no test-time compute, 128K thinking budget, default sampling. Includes a minor prompt addendum encouraging extensive tool use and writing tests first.
- Terminal-Bench: Terminus-2 agent, average over 11 runs (6 without thinking, 5 with 32K thinking budget).
- OSWorld-Verified: 100 max steps, averaged across 4 runs with a 128K total thinking budget and 2K per-step configuration.
- AIME / MMMLU: averages over multiple runs using default sampling and 128K thinking budgets.
The post emphasizes coding parity with Sonnet 4 and computer-use gains relative to Sonnet 4 under these scaffolds. Users should replicate with their own orchestration, tool stacks, and thinking budgets before generalizing.
Key Takeaways
- Haiku 4.5 delivers Sonnet-4-level coding performance at one-third the cost and more than twice the speed.
- It surpasses Sonnet 4 on computer-use tasks, improving responsiveness in Claude for Chrome and multi-agent flows in Claude Code.
- Recommended orchestration: use Sonnet 4.5 for multi-step planning and parallelize execution with multiple Haiku 4.5 workers.
- Pricing is $1/$5 per million input/output tokens; available via Claude API, Amazon Bedrock, and Google Cloud Vertex AI.
- Released under ASL-2 with a lower measured misalignment rate than Sonnet 4.5 and Opus 4.1 in Anthropic’s tests.
Editorial Comments
Anthropic’s positioning of Claude Haiku 4.5 is strategically sound: by delivering similar levels of coding performance to Claude Sonnet 4 at one-third the cost and more than twice the speed, while surpassing Sonnet 4 on computer use, the company gives devs a clean planner–executor split—Sonnet 4.5 for multi-step planning and a pool of Haiku 4.5 workers for parallel execution—without forcing architectural changes (“drop-in replacement” across API, Amazon Bedrock, Vertex AI). The ASL-2 release, coupled with a documented lower misalignment rate than Sonnet 4.5 and Opus 4.1, lowers the friction for enterprise rollout where safety gates and cost envelopes dominate deployment math.
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The post Anthropic Launches Claude Haiku 4.5: Small AI Model that Delivers Sonnet-4-Level Coding Performance at One-Third the Cost and more than Twice the Speed appeared first on MarkTechPost.
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