Claude Haiku 4.5 for Coding — Benchmark and Cost Guide

Haiku 4.5 scores 73.3% on SWE-bench Verified — 5.9 points below Sonnet 4.6’s 79.2% — and costs three times less per token. That gap disappears on single-file edits, boilerplate, test generation, and PR review. It opens wide on multi-file reasoning, architectural decisions, and complex debugging. Treat it as the executor layer in your stack, not the orchestrator.

Who this is for

Developers building AI coding tools who need to control inference cost at scale, or individuals running Claude Code who want to know when the automatic Haiku routing is the right call — and when to route around it. If every task you run crosses multiple files or requires sustained cross-context reasoning, start with Sonnet 4.6.

What we looked at

Benchmarks sourced from Anthropic’s official Haiku 4.5 announcement, Qodo’s independent study across 400 real pull requests, and community data from Hacker News discussion #45603947. Pricing and specs from Anthropic’s documentation on 2026-05-16.

What it’s good at

Single-file edits and boilerplate

Most day-to-day coding requests are bounded: add a function, write a test, rename a variable, translate a bash command. On these, Haiku 4.5 runs close to Sonnet parity. Qodo ran 400 real pull requests through Haiku 4.5 and Sonnet 4 head-to-head and Haiku 4.5 came out ahead in 55.19% of comparisons. In thinking mode (4,096-token budget), that rose to 58% against Sonnet 4.5 Thinking with an average quality score of 7.29/10.

Qodo found this compelling enough to make Haiku 4.5 one of three default reviewers in their system — alongside GPT-5.2 for deep analysis and Gemini 2.5 Pro as the primary everyday reviewer. Haiku handles the high-volume repositories and teams that need precise, minimal suggestions. A layered model architecture where each model earns its slot is a meaningful endorsement.

Bash scripting, Go, and PHP

Community benchmarks flag bash scripting, Go, and single-file PHP as consistent Haiku strengths. These are domains where tasks arrive well-specified and bounded — write a script that does X, add a PHP endpoint for Y, add a Go handler. Haiku handles them reliably.

Real-time completions and agentic pipelines

At ~93 tokens/sec output with a 0.77s time-to-first-token, Haiku 4.5 runs ~2× faster than Sonnet 4.6 (~47 t/s, 1.37s TTFT). For interactive autocomplete or any loop where latency is perceptible, that gap is felt. Claude Code routes tasks to Haiku automatically when speed and cost efficiency matter more than raw capability.

Anthropic’s own claim is “similar coding performance to Sonnet 4 at one-third the cost and more than twice the speed.” The Qodo data backs the coding-performance part for bounded tasks; the cost and speed claims are straightforward arithmetic from the spec sheet.

At volume, routing even a fraction of requests to Haiku adds up fast.

Where it falls short

Multi-file reasoning

Ask Haiku to trace a bug across six files, understand how a middleware chain transforms a request before it reaches a handler, or reason about an unfamiliar module’s side effects — this is where the 5.9-point SWE-bench gap begins to show. The harder the cross-file reasoning requirement, the wider the gap.

Context window amplifies this: Haiku’s 200k tokens versus Sonnet 4.6’s 1M means that large-codebase agents doing whole-repository analysis can hit a ceiling that doesn’t exist on Sonnet.

The verbosity anti-pattern

This is the concrete gotcha. On loose prompts — “refactor this module” or “improve this code” — community reports show Haiku 4.5 generating noticeably more code than Sonnet for the same task (HN #45603947). The issue is not hallucination. Haiku over-generates to cover for lower confidence. More code means more surface area for bugs and more noise in code review.

The fix is tight prompts with explicit scope constraints: “add a function that does X, modify only this file, no other changes.” That works — but it changes the prompting discipline required to use Haiku well. If your team writes open-ended prompts, Sonnet is safer.

React, architecture, and VFX domain code

React component trees with cross-component state, architectural tradeoff questions, sustained multi-step debugging sessions, and specialized domains with heavy semantic context (VFX pipeline code is the community-cited example) are consistently flagged as Haiku weak spots. These tasks belong on Sonnet.

Speed and cost

	Claude Haiku 4.5	Claude Sonnet 4.6
Input price	$1.00 / 1M tokens	$3.00 / 1M tokens
Output price	$5.00 / 1M tokens	$15.00 / 1M tokens
Context window	200k tokens	1M tokens
SWE-bench Verified	73.3%	79.2%
Output speed	~93 tokens/sec	~47 tokens/sec
TTFT	~0.77s	~1.37s
Best for	Execution, single-file, volume	Orchestration, multi-file, complex

Batch API reduces both models by 50%: Haiku input drops to $0.50/1M, Sonnet to $1.50/1M. Prompt cache reads at 0.1× the input price — a 90% discount on any context your agent reads repeatedly. A coding agent with a large, stable system prompt and shared code context gets most of the benefit from caching on either model.

A worked example: 10,000 requests per day, 800 input tokens and 200 output tokens each. All-Haiku runs at $9/day. All-Sonnet runs at $26/day. Routing half your requests to Haiku lands around $18/day. At 1M requests/day, those ratios become meaningful infrastructure decisions — see the real cost of running an AI agent team for the full breakdown.

Full pricing: Anthropic pricing

Context window: does 200k matter?

For most single-file and PR-review tasks, no. 200k tokens handles any realistic codebase slice you’d send in one request — a file, a diff, a few related modules. Where the ceiling matters is large-codebase agents that load multiple large files in a single context window, or long multi-turn agentic sessions that accumulate history. If your agent regularly hits 150k+ tokens in a single call, Sonnet’s 1M window is the reason to pay the premium.

Claude Haiku 4.5 verdict

Use Haiku 4.5 for executor-layer work: single-file edits, test generation, PR review, bash scripting, boilerplate, real-time completions, and any high-volume pipeline where cost and latency dominate quality concerns. At 73.3% on SWE-bench Verified, 93 t/s, and $1/1M input tokens, it is the right model for that layer.

Use Sonnet 4.6 when the task crosses file boundaries, requires architectural judgment, involves complex debugging, or deals with domains that carry heavy semantic context. The 5.9-point benchmark gap is real and grows with task complexity.

If you are building a coding tool: route bounded, well-specified tasks to Haiku and orchestrate on Sonnet. Claude Code already does this. If you are picking between AI coding tools, Best AI Coding CLI in 2026 ranks six options side by side. If you are an individual developer: write tight, scoped prompts on Haiku and reach for Sonnet when you need the model to reason its way through something it hasn’t been pointed at.