Claude Haiku 5.5 vs. Local Models: Which Is Cheaper for Coding?

Which is cheaper for coding: Claude Haiku 5.5 or a local model?

Haiku 5.5 and local models are both so cheap that the two honest answers land in the same place. In the cloud, Claude Haiku 5.5 prices coding-capable inference at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100K tokens, a roughly 75% average cut from Haiku 4.5's already-cheap rates. On your own hardware, a well-chosen open-weight coding model can run for $0.06 to $0.55 per million output tokens in raw electricity, depending on the model's architecture. Neither number is the whole story. But together they mean the marginal cost of running an internal coding tool has fallen so far that cost is barely the question anymore. The question is what you build, and who's accountable for it once it's running.

This isn't trivia. Teams deciding whether to build an internal tool instead of renting another SaaS seat used to run that decision through a token-cost spreadsheet. That spreadsheet now returns numbers so small they round to zero. What's left is engineering time, maintenance, and governance, and that's a different conversation than the one most build-vs-buy memos are still having.

What do you get from Claude Haiku 5.5: frontier-adjacent coding at near-zero marginal cost?

Anthropic released Haiku 5.5 on October 7, 2026, and its pricing tells you exactly what Anthropic thinks the model is for. At $0.10/$0.50 per million tokens for typical prompt lengths, it costs a fraction of what Sonnet- or Opus-tier models charge, and it's priced to be called constantly, not sparingly.

Here's what you're actually buying. Haiku 5.5 is explicitly positioned as a coding subagent, not a lead model for complex, end-to-end agentic coding. On Terminal-Bench 4.0, it scores 39.2% against Sonnet 5.5's 70.6%. Anthropic recommends it for narrowly scoped, high-volume work: compaction, summarization, parallel subagent tasks, the repetitive glue work a coding agent generates by the thousands of calls per session.

Haiku 4.5 set this pattern a year earlier. Released at $1 per million input tokens and $5 per million output tokens, it matched Claude Sonnet 4's performance on reasoning, coding, and computer-use tasks while costing roughly a third as much and running more than twice as fast. It scored 73.3% on SWE-bench Verified, essentially tying Sonnet 4's 72.7%. Haiku 5.5 isn't trying to repeat that trick at the flagship-coding level. It's pushing the cost of the supporting work down instead. Together, the two releases show the same company cutting price aggressively at two different tiers of capability, a year apart.

How did we get here with the LLMflation curve?

Haiku's pricing isn't an outlier. It's a data point on a well-documented curve. Andreessen Horowitz calls it LLMflation: for a fixed level of model performance, inference cost has fallen roughly 10x per year since 2021. A GPT-3-equivalent model (MMLU around 42) cost $60 per million tokens when GPT-3 became publicly accessible in November 2021. By late 2024, the cheapest model clearing that same performance bar cost $0.06 per million tokens, a 1,000x drop in three years.

Epoch AI's independent analysis finds the same direction with more texture: the rate of decline ranges from 9x to 900x per year depending on which benchmark and performance tier you track, with the price to match GPT-4 on PhD-level science questions falling about 40x per year. The two studies disagree on exact magnitude but agree completely on direction and rough order of size. That's the backdrop that makes Haiku 5.5's pricing predictable rather than surprising. It's not a one-off promotion. It's a trend line continuing.

What does it actually cost to run a coding model locally?

Cloud pricing isn't the only side of this. Open-weight coding models have closed much of the capability gap, and running frontier-capable models on hardware you own has gotten genuinely cheap to measure.

Qwen3-Coder-Next, an 80B-parameter mixture-of-experts model with only 3B active parameters, scores over 70% on SWE-bench Verified using an SWE-Agent scaffold, comparable to models with 10 to 20 times more active parameters. That's within range of Haiku 4.5's cloud benchmark, running on hardware a mid-sized team can own outright.

The cost side has been measured carefully too. One wall-socket-calibrated study on an Apple M3 Ultra Mac Studio found marginal electricity cost per million output tokens ranging from about $0.06 for a small 4B dense model up to $0.55 for a mid-size 27B dense model, with well-quantized mixture-of-experts models landing around $0.09 to $0.11. The same study found that in real day-to-day use, a dense 27B model cost roughly 10 times more per token than similarly sized MoE models, a reminder that parameter count alone doesn't predict local cost. Architecture and throughput matter more than raw size. For a deeper look at what current Apple Silicon hardware changes about this math, see the case for owning your AI compute.

None of this is free. It excludes the amortized cost of the machine itself, and whether local hardware beats renting depends almost entirely on usage volume. Industry rules of thumb put the break-even around 250 to 500 hours of use per month, and a representative RTX 4090 workstation (roughly $2,800 all-in) reaches full-TCO break-even around 20 months at 8 hours of daily use against a $0.50/hour cloud rate. Electricity itself is a minor, predictable line: a 450-watt workstation run 8 hours a day costs roughly $18 a month at US residential rates, or around $40 a month at German rates.

How do Haiku 5.5 and local models compare side by side on cost?

Line the two options up and the comparison sharpens:

• Cloud sticker price. Haiku 5.5 runs $0.10/$0.50 per million tokens for typical prompts, among the cheapest frontier-adjacent coding tiers on the market.
• Local marginal cost. Well-quantized MoE models run $0.09 to $0.11 per million output tokens in electricity alone once hardware is already owned.
• Capability ceiling. Haiku 5.5 is a subagent model (39.2% Terminal-Bench); Haiku 4.5 and Qwen3-Coder-Next both land around 70-73% on SWE-bench Verified, a tier closer to true lead-agent capability.
• Hidden cost. Cloud pricing has no upfront outlay but scales linearly with usage forever; local requires hardware capital that only pays off past hundreds of hours a month of sustained use.
• Trend direction. Both are falling. Cloud pricing is riding a documented 10x-per-year curve; local inference gets cheaper every time a smaller open-weight model matches a bigger one's benchmark score.

The practical read: at low or spiky volume, Haiku 5.5's near-zero per-call price beats owning anything. At sustained, high-volume, or privacy-sensitive use, local hardware's fixed monthly electricity bill starts beating cloud billing within a year or two, as teams weighing rate limits on hosted coding agents have already discovered the hard way.

Why does this change the build-vs-buy calculus?

For years, the strongest argument against building an internal tool was simple: inference was too expensive to justify replacing a SaaS subscription. That argument is losing its force fast.

Retool's 2026 survey captures the shift in numbers:

• 35% of teams have already replaced at least one SaaS tool with something they built themselves, and 78% plan to build more this year.
• 60% of builders across seniority levels say they've built something outside IT's oversight in the past year.
• Among teams that shipped production software built with AI assistance, 51% report it's currently in active use, and roughly half of those report saving six or more hours a week.

That shift was already underway before Haiku 5.5 shipped. Falling token costs don't start the trend, they remove the last financial objection to it. When the marginal cost of running your own tool drops below a rounding error, the build-vs-buy memo stops being about dollars per million tokens and starts being about engineering hours, maintenance ownership, and whether the thing someone built on a Tuesday afternoon should keep running unsupervised.

Is the real cost still inference, or is it governance?

Here's the uncomfortable part. If sixty percent of builders are shipping tools outside IT's view, and the cost of doing so keeps falling, the bottleneck on responsible internal software isn't compute anymore. It's knowing what's running, who owns it, where the data goes, and what happens when the person who built it leaves the company. Cheap inference makes shadow AI easier to create and harder to track at the same volume. It's the same dependency problem that shows up when a single vendor controls the model hub your team relies on.

That's a governance problem, not a pricing problem, and it's a different kind of discipline than watching a token meter. Teams serious about owning their internal tools instead of renting them eventually need a layer that tracks what's been built, by whom, and against what data.

How do you pick the cheapest AI model for coding?

The honest answer depends on how you use the model:

1. Spiky or low-volume use. Call Haiku 5.5 or a comparable cloud subagent model. At $0.10/$0.50 per million tokens, there's no hardware to buy and no idle capacity to waste.
2. Sustained, high-volume internal tooling. Run an open-weight model like Qwen3-Coder-Next locally. Once you're past a few hundred hours a month of use, electricity costs undercut cloud billing by an order of magnitude.
3. Privacy- or compliance-sensitive codebases. Default to local regardless of volume. The cost argument favors cloud only at low usage, and that math doesn't account for what leaves your network when you call an API.
4. Complex, end-to-end agentic coding. Don't reach for the cheapest tier at all. Haiku-class models are built for subagent work; lead coding still belongs to Sonnet- or Opus-class models, cloud or local equivalents.
5. Any tool you plan to run for more than a few months. Build the governance layer before you build the tool. The cost curve already favors you. The risk curve doesn't wait for you to catch up.

The falling cost of inference was never going to be the hard part of owning your own software. It just took until Haiku 5.5's price sheet landed next to a wall-socket electricity measurement for that to become obvious.