Claude Haiku 4.5 vs GPT-5.6 Luna for Coding

Claude Haiku 4.5 vs GPT-5.6 Luna for coding is the low-cost cross-provider decision for fast, bounded work. Haiku is Anthropic's fastest current Claude model. Luna is OpenAI's cost-sensitive GPT-5.6 tier. Both accept text and images, call tools, and can power coding workflows, but they differ sharply in context, reasoning controls, protocol fit, and token price.

Our starting rule is Haiku for Claude-native support tasks and Luna for high-volume Codex or Responses work. Luna is much cheaper on the published rate cards, while Haiku fits Claude Code and Anthropic-native tool loops directly. Neither fact proves a universal coding-quality winner.

Claude Haiku 4.5 vs GPT-5.6 Luna at a glance

The short answer is: choose the native workflow first, then compare accepted-task cost. If both models pass the same narrow task, Luna's current token economics are difficult to ignore. If the task depends on Claude Code or Anthropic-native behavior, Haiku is the cleaner first test.

Specifications and API prices

Prices are USD per million tokens. The official rows describe standard direct API rates. The OmniaKey row is the current gateway quote, not a subscription allowance or a permanent promise. Check the live pages for Claude Haiku 4.5, GPT-5.6 Luna, and the model catalog before setting a production budget.

Anthropic's dated Haiku ID is claude-haiku-4-5-20251001; claude-haiku-4-5 is the convenient Claude API alias. OmniaKey exposes the shorter model ID shown in its catalog. Pin the exact ID your provider documents when reproducibility matters.

What the same coding workload costs

Consider a successful run with 100,000 uncached input tokens and 20,000 output tokens:

At the current OmniaKey rates, the same token counts cost:

This is rate-card arithmetic, not completed-task evidence. A $0.044 Luna attempt costs more than a $0.20 Haiku attempt if Luna needs five failed retries and Haiku passes once. The useful unit is the accepted change, not the first response.

Prompt caching preserves Luna's price advantage

Now reuse 100,000 cached tokens, add 10,000 uncached tokens, and produce 20,000 output tokens. Excluding the earlier cache-write request:

Both providers price cache writes at 1.25 times the base input rate for the shortest documented cache path. Anthropic also offers a one-hour write at twice Haiku's base input rate. OpenAI's model page lists Luna cache writes at $0.25 per million tokens.

Caching helps when system instructions, tool schemas, repository guidance, or stable source material repeat. It does not make irrelevant context free. A smaller, better-selected prefix remains easier to reason over and cheaper to invalidate.

Where Haiku is the stronger starting point

Choose Haiku first when the workflow is already Claude-native and the task is tightly bounded:

• a Claude Code subagent searching for a known symbol or file;
• classifying test failures before a stronger model investigates them;
• summarizing a focused diff or a small set of logs;
• making a mechanical edit with deterministic tests;
• extracting structured facts from a document or screenshot;
• handling a short tool loop where 200K context is sufficient.

Anthropic describes Haiku 4.5 as its fastest model with near-frontier intelligence. It supports manual extended thinking but not the adaptive-thinking mode used by newer Claude tiers. Extended thinking is useful when a bounded task needs more reasoning, but extra thinking can erase part of the latency and cost advantage. Measure the exact configuration you deploy.

Haiku's 64K maximum output is generous for code generation, but the 200K total context is the binding limit for large repository sessions. Retrieve the relevant files rather than treating the context window as a repository archive.

The Claude Code model guide explains when Haiku is a good support model and when Sonnet, Opus, or Fable is the safer escalation.

Where Luna is the stronger starting point

Choose Luna first when price, throughput, or the OpenAI-native tool stack defines the job:

• high-volume extraction, classification, and transformation;
• schema-constrained output through Responses or Chat Completions;
• a focused Codex task with explicit acceptance tests;
• repetitive migration edits with known call sites;
• generating test cases from a fixed contract;
• processing larger relevant inputs that do not fit Haiku's 200K window.

OpenAI positions Luna for cost-sensitive, high-volume workloads and labels it fast. Its reasoning effort ranges from none through max, with medium as the API default. Start at the lowest effort that still passes the acceptance check; a cheap model running at unnecessary effort is still wasted work.

Luna's model page lists supported Responses tools including web search, file search, hosted shell, apply patch, computer use, MCP, and structured outputs. Those are platform capabilities, not a guarantee that every client or gateway exposes every provider-hosted tool. Verify the exact route rather than inferring tool support from the model name.

The GPT-5.6 model guide for Codex covers when to move from Luna to Terra or Sol.

Long context changes the decision

Haiku cannot accept a 300,000-token input inside its 200K context window. Luna can, but its direct API has a separate long-context rule: prompts above 272K input tokens are billed at twice the input rate and 1.5 times the output rate for the full request.

For 300,000 input tokens and 40,000 output tokens on Luna:

input  = 0.3 x $0.40 = $0.120
output = 0.04 x $1.80 = $0.072
total  = $0.192

That still does not justify sending 300K tokens by default. More context can dilute evidence, increase cache churn, and make failures harder to diagnose. Use Luna's larger window when the relevant task genuinely exceeds Haiku's capacity, not as a substitute for retrieval.

Speed claims need a matched test

The official pages call Haiku the fastest Claude model and label Luna fast. Those statements compare each model within its own provider family. They do not establish a cross-provider tokens-per-second winner under the same region, load, protocol, prompt, and reasoning budget.

Measure at least four latency values:

1. time to first token;
2. output tokens per second;
3. time to the first valid tool call;
4. wall-clock time until the acceptance check passes.

For interactive coding, the fourth measure matters most. A fast stream followed by an invalid patch is slower than a slightly longer run that passes immediately.

Reasoning controls are not equivalent

Haiku uses manual extended thinking with a configured token budget. Luna uses named effort levels from none to max. A Haiku thinking budget and Luna medium do not represent matched compute, latency, or quality.

Run two comparison tracks:

1. Provider-default track: use each API's documented default behavior.
2. Budgeted track: cap total spend or wall time, then let each model use its own native reasoning control.

Do not call both settings “medium” and assume the experiment is fair. Record actual billed tokens and elapsed time. When the native harness is part of the decision, label Claude Code versus Codex as an end-to-end agent comparison rather than a pure model test.

The Claude Code vs Codex comparison covers that separate harness decision.

A reproducible low-cost coding evaluation

Use work that is cheap to judge and difficult to bluff:

1. Mechanical refactor: update known call sites without changing a public interface.
2. Focused bug: fix a failing reproduction with one hidden edge case.
3. Structured transformation: convert a fixture into a schema-validated output.
4. Test generation: add cases for a documented contract and run the suite.
5. Bounded review: identify seeded correctness issues in a small patch.

For every run:

1. Start from the same commit and supply the same relevant files.
2. Match tool access, permissions, task wording, and stop conditions.
3. Pin claude-haiku-4-5 and gpt-5.6-luna rather than moving family aliases.
4. Run provider defaults first and explicit reasoning settings second.
5. Repeat each configuration at least three times.
6. Record acceptance result, input, cache, output, latency, retries, and correction time.

Use the Claude Code guide for the Anthropic-compatible route and the Codex CLI guide for the Responses-compatible route. OmniaKey provides both IDs under one balance, but the native protocols and agent behavior remain different.

Which model is better for vibe coding?

“Vibe coding” covers very different workflows. For disposable prototypes with clear checks, Luna's low current rate makes repeated exploration inexpensive. For a Claude Code session where tool behavior and the existing Anthropic workflow matter, Haiku is the native low-cost option.

Neither should be allowed to merge unreviewed changes because it is cheap or fast. Define the test, lint rule, screenshot, schema, or human review that turns an output into an accepted result.

A practical routing policy

Use this order:

1. Honor the protocol constraint: Claude Code points to Haiku; Codex or Responses points to Luna.
2. For a small task with no protocol constraint, try Luna when token cost and throughput dominate.
3. Try Haiku when Claude-native tool behavior or an existing Claude workflow matters more.
4. Keep the model only when it passes the same deterministic acceptance check.
5. Escalate ambiguous debugging, architecture, or high-risk changes to Sonnet/Terra, then Opus/Sol, instead of forcing the cheap tier through repeated failures.

The Sonnet 5 vs GPT-5.6 Terra comparison owns the balanced daily-model decision. The Opus 5 vs GPT-5.6 Sol comparison owns the harder frontier decision. The broader coding-model guide remains the family-level hub.

Final verdict

Choose Claude Haiku 4.5 for bounded Claude-native work, small support-agent roles, and tasks where Anthropic protocol fit matters more than the lowest token price. It is the fastest current Claude model, supports manual extended thinking, and provides 200K context with up to 64K output.

Choose GPT-5.6 Luna for high-volume, well-specified work through Codex, Responses, or an OpenAI-compatible client. It has the lower direct and current OmniaKey rate, a 1.05M context window, and six reasoning-effort levels.

Luna wins the rate-card comparison. The model that wins your workflow is the one that repeatedly passes the same acceptance test at the lower completed-task cost.

Frequently asked questions

Is GPT-5.6 Luna better than Claude Haiku 4.5 for coding?

Not universally. Luna is the stronger price and context starting point for well-specified, high-volume work. Haiku is the native low-cost Claude choice for bounded Claude Code and Anthropic-compatible workflows. A matched accepted-task test is required for a quality claim.

Which model is cheaper?

Luna on the current direct rate cards: $0.20 input and $1.20 output per million tokens versus Haiku's $1 input and $5 output. Luna also has the lower current OmniaKey rate. Retries, reasoning, tools, and developer correction time still determine completed-task cost.

Which model is faster?

Anthropic calls Haiku its fastest model, while OpenAI labels Luna fast and recommends it for the lowest-cost, lowest-latency GPT-5.6 workloads. Those are within-family positions. Measure time to first token, output rate, tool-call latency, and accepted-task time on your actual route.

Which model has more context?

Luna lists a 1,050,000-token context window and 128,000 maximum output. Haiku lists 200,000 context tokens and 64,000 maximum output. Luna's direct price increases for the full request above 272K input tokens.

Can I use Haiku in Codex or Luna in Claude Code?

They are not native crossovers. Claude Code is designed around Claude and Anthropic-compatible configuration; Codex is designed around GPT models and Responses-compatible providers. A model-agnostic client may expose both, but protocol translation and tool support must be verified.

When should I move to a stronger model?

Escalate when the cheap model had the relevant evidence and tools but still missed the root cause, architectural constraint, or acceptance criteria. Move from Haiku to Sonnet and from Luna to Terra first; reserve Opus or Sol for ambiguity and high-cost failures.

Sources checked

• Anthropic models overview
• Anthropic API pricing and prompt caching
• Anthropic extended thinking
• GPT-5.6 Luna model
• Official OpenAI API pricing

Information was verified August 18, 2026. Model access, prices, aliases, context rules, reasoning controls, and tool support can change; verify first-party documentation and the live catalog before production use.