{"id":1027,"date":"2026-10-14T07:32:21","date_gmt":"2026-10-14T07:32:21","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=1027"},"modified":"2026-10-14T07:32:23","modified_gmt":"2026-10-14T07:32:23","slug":"claude-haiku-5-5-vs-gpt-6-luna","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/claude-haiku-5-5-vs-gpt-6-luna","title":{"rendered":"Claude Haiku 5.5 vs GPT-6 Luna: Price & Which to Pick (2026)"},"content":{"rendered":"\n\n\n
\n\n

Comparison \u00b7 October 2026<\/p>\n\n

\nClaude Haiku 5.5 (Anthropic API) and GPT-6 Luna (OpenAI API) are two chat models teams actually route in 2026. This is a decision guide, not a leaderboard dump: published API rates, three workload cost scenarios, live writing and coding snippets, and a pick matrix you can rerun. If you want both without juggling tabs, use a\nmulti-model AI workspace<\/a>\nor start on\ni10X<\/a>.\n<\/p>\n\n

\nQuick verdict<\/strong>\n

Pick Claude Haiku 5.5 if:<\/strong> you want a deeper empty-list coding writeup, and stricter false-premise handling.<\/p>\n

Pick GPT-6 Luna if:<\/strong> you want the larger window (1.05M vs 1M).<\/p>\n

Best default for many teams:<\/strong> route by task and keep both available. See the decision matrix below.<\/p>\n

Data checked: 2026-10-12. Prices and model cards change. Verify live API and vendor pages.<\/em><\/p>\n<\/div>\n\n

\n\n\n\n\n\n\n

1M<\/strong><\/p><\/td>\n

Claude Haiku 5.5 context (API)<\/p><\/td>\n<\/tr>\n

1.05M<\/strong><\/p><\/td>\n

GPT-6 Luna context (API)<\/p><\/td>\n<\/tr>\n

$0.10 \/ $0.50<\/strong><\/p><\/td>\n

Claude Haiku 5.5 input\/output per 1M tokens (API pricing, 2026-10-12)<\/p><\/td>\n<\/tr>\n

$0.10 \/ $0.50<\/strong><\/p><\/td>\n

GPT-6 Luna input\/output per 1M tokens (API pricing, 2026-10-12)<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n

\n\"Bar\n
Figure 1.<\/strong> Where each model wins on relative axes (context, output cost efficiency, writing tightness, coding micro-test, cache-read price). Higher is stronger for that axis. Chart: i10X.<\/figcaption>\n<\/figure>\n\n
\n\n

Persona picker<\/h2>\n\n\n\n\n\n\n\n

You are\u2026<\/p><\/th>\n

Start with<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Writer \/ marketer<\/p><\/td>\n

Either; A\/B on brand voice<\/p><\/td>\n

Claude Haiku 5.5: Complete, polite “Hi there,” all facts, under 120 words, no greeting fluff. GPT-6 Luna: Same facts in a short note, no name slot, under 120 words.<\/p><\/td>\n<\/tr>\n

Developer \/ agent builder<\/p><\/td>\n

GPT-6 Luna for huge repos; Claude Haiku 5.5 for cheap loops<\/p><\/td>\n

Window 1.05M vs 1M. Micro-test: Named ZeroDivisionError. None vs ValueError. Guard must come before the divide. \/ Named ZeroDivisionError. Short ValueError guard. No extra options.<\/p><\/td>\n<\/tr>\n

Researcher \/ analyst<\/p><\/td>\n

GPT-6 Luna<\/p><\/td>\n

1M vs 1.05M on the cards we pulled.<\/p><\/td>\n<\/tr>\n

Budget \/ high volume<\/p><\/td>\n

Either on chat<\/p><\/td>\n

Matched chat cost in this recipe. Check output and cache.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

What we are comparing (exact versions)<\/h2>\n

This page compares two specific API models, not vague brand names and not last quarter’s IDs. Nearby versioned pages:\nClaude Haiku 4.5 vs GPT-5.6 Luna<\/a>, Gemini 3.7 Flash vs GPT-5.6 Luna<\/a>, Claude Sonnet 5.5 vs GPT-6.1 Sol<\/a>, MiniMax M3 vs GPT-5.6 Luna<\/a>, Grok 4.7 vs GPT-6.1 Sol<\/a>, and Claude Opus 5.5 vs GPT-6.1 Sol<\/a>.<\/p>\n\n\n\n\n\n\n\n\n

Field<\/p><\/th>\n

Claude Haiku 5.5<\/p><\/th>\n

GPT-6 Luna<\/p><\/th>\n<\/tr>\n

Provider<\/p><\/td>\n

Anthropic<\/p><\/td>\n

OpenAI<\/p><\/td>\n<\/tr>\n

API model<\/p><\/td>\n

Claude Haiku 5.5 (Anthropic API)<\/code><\/p><\/td>\n

GPT-6 Luna (OpenAI API)<\/code><\/p><\/td>\n<\/tr>\n

Listed card name<\/p><\/td>\n

Anthropic: Claude Haiku 5.5<\/p><\/td>\n

OpenAI: GPT-6 Luna<\/p><\/td>\n<\/tr>\n

Family \/ tier<\/p><\/td>\n

Claude 5.5 fast \/ cheap tier<\/p><\/td>\n

GPT-6 fast \/ cheap tier<\/p><\/td>\n<\/tr>\n

App vs API note<\/p><\/td>\n

Also in Claude apps; this article uses the API model above<\/p><\/td>\n

Also in ChatGPT-family apps; this article uses the API model above<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

If a page still compares older IDs as if they were these cards, treat it as historical. For routing across many models, see\nAI model routing<\/a>.<\/p>\n\n

\n\n

Spec sheet (API card, 2026-10-12)<\/h2>\n\n\n\n\n\n\n\n\n

Spec<\/p><\/th>\n

Claude Haiku 5.5<\/p><\/th>\n

GPT-6 Luna<\/p><\/th>\n<\/tr>\n

Context window<\/p><\/td>\n

1,000,000 tokens<\/p><\/td>\n

1,050,000 tokens<\/p><\/td>\n<\/tr>\n

Input modalities (card)<\/p><\/td>\n

text, image, file<\/p><\/td>\n

text, image, file<\/p><\/td>\n<\/tr>\n

Output<\/p><\/td>\n

text<\/p><\/td>\n

text<\/p><\/td>\n<\/tr>\n

Open weights<\/p><\/td>\n

No<\/p><\/td>\n

No<\/p><\/td>\n<\/tr>\n

Vendor positioning (card)<\/p><\/td>\n

Small, fast successor to Haiku 4.5; high-volume summarization, subagents, browser use<\/p><\/td>\n

Fast, cost-efficient GPT-6 tier below Sol; high-volume chat, classification, lightweight agents<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

We did not invent max-output, reasoning-mode, or realtime-search rows. Those fields were not on the cards we pulled for this pair. If your product depends on a specific tool or effort switch, confirm it on the live vendor page before you ship.<\/p>\n\n

\n\n

Pricing and real workload cost<\/h2>\n

List prices are easy to game. Workload cost is what you feel. Numbers below use published per-million rates as of 2026-10-12<\/strong>. Verify live<\/strong> before you budget.<\/p>\n\n\n\n\n\n\n

Price<\/p><\/th>\n

Claude Haiku 5.5<\/p><\/th>\n

GPT-6 Luna<\/p><\/th>\n<\/tr>\n

Input \/ 1M tokens<\/p><\/td>\n

$0.10<\/p><\/td>\n

$0.10<\/p><\/td>\n<\/tr>\n

Output \/ 1M tokens<\/p><\/td>\n

$0.50<\/p><\/td>\n

$0.50<\/p><\/td>\n<\/tr>\n

Cache read \/ 1M<\/p><\/td>\n

$0.01<\/p><\/td>\n

$0.01<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Same input sticker ($0.10). Matched output at $0.50. Cache reads match on this pull.<\/p>\n\n\n\n\n\n\n

Scenario<\/p><\/th>\n

Assumed tokens<\/p><\/th>\n

Est. Claude Haiku 5.5<\/p><\/th>\n

Est. GPT-6 Luna<\/p><\/th>\n<\/tr>\n

Chat turn<\/p><\/td>\n

1k in + 0.5k out<\/p><\/td>\n

$0.00035<\/p><\/td>\n

$0.00035<\/p><\/td>\n<\/tr>\n

Repo \/ doc review<\/p><\/td>\n

80k in + 4k out<\/p><\/td>\n

$0.010<\/p><\/td>\n

$0.010<\/p><\/td>\n<\/tr>\n

Agent loop<\/p><\/td>\n

200k in (50% cached) + 20k out<\/p><\/td>\n

$0.021<\/p><\/td>\n

$0.021<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using published API list rates (2026-10-12). Chat is 1k in + 0.5k out. Repo review is 80k in + 4k out. Agent loop is 200k in with 50% cache hits + 20k out. Chart: i10X.<\/figcaption>\n<\/figure>\n

On the cached agent recipe, neither wins ($0.021 vs $0.021). Re-run the math if you cache harder. For subscription stacks, see\nAI subscription stack cost<\/a>.<\/p>\n\n

\n\n

Performance by job (not one score)<\/h2>\n

Public benches disagree by harness, effort mode, and date. We are not inventing index numbers we do not have for this pair. Treat vendor cards as positioning, treat our snippets as a small live pack, and prefer your own side-by-side on your prompts. Method:\nside-by-side AI comparison<\/a>.<\/p>\n\n

Coding and agents<\/h3>\n

Small, fast successor to Haiku 4.5; high-volume summarization, subagents, browser use. Fast, cost-efficient GPT-6 tier below Sol; high-volume chat, classification, lightweight agents. Those are positioning lines, not benches. Our micro-test: Named ZeroDivisionError. None vs ValueError. Guard must come before the divide. Named ZeroDivisionError. Short ValueError guard. No extra options. Window size and cache price matter more on long agent loops than this snippet.<\/p>\n\n

Writing and tone<\/h3>\n

Claude Haiku 5.5 for Complete, polite “Hi there,” all facts, under 120 words, no greeting fluff. GPT-6 Luna for Same facts in a short note, no name slot, under 120 words. If your brand wants send-ready client email, prefer the tighter letter. If you hate filler, prefer the shorter note.<\/p>\n\n

Research, math, reasoning<\/h3>\n

We did not run a science-QA pack, so we will not fake one. False-premise: Strong geology refuse, then Earth-realistic protein routes and lunar ice as support, not cheese. Refuse in two sentences. Suggests Earth beans and tofu. Thin as a plan. For publishable work, add\nmulti-model hallucination checks<\/a>.<\/p>\n\n

Multimodal and long context<\/h3>\n

Claude Haiku 5.5 lists text, image, file in, text out. GPT-6 Luna lists text, image, file in, text out. Context is 1,000,000 tokens vs 1,050,000 tokens. Giant packs go to the larger window. Audio or video only if the card actually lists it.<\/p>\n\n

Speed<\/h3>\n

No tok\/s in this pack. Measure p50 from your API region. Do not ship on a third-party screenshot.<\/p>\n\n\n\n\n\n\n\n\n\n

Job<\/p><\/th>\n

Edge<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Hard coding \/ agents<\/p><\/td>\n

Claude Haiku 5.5<\/p><\/td>\n

Named ZeroDivisionError. None vs ValueError. Guard must come before the divide. Named ZeroDivisionError. Short ValueError guard. No extra options.<\/p><\/td>\n<\/tr>\n

Everyday writing<\/p><\/td>\n

Split<\/p><\/td>\n

Complete, polite “Hi there,” all facts, under 120 words, no greeting fluff. Same facts in a short note, no name slot, under 120 words.<\/p><\/td>\n<\/tr>\n

Long docs \/ multimodal<\/p><\/td>\n

GPT-6 Luna<\/p><\/td>\n

1.05M vs 1M. Input set: text, image, file vs text, image, file.<\/p><\/td>\n<\/tr>\n

Realtime \/ conversational<\/p><\/td>\n

Product-dependent<\/p><\/td>\n

Product-dependent. Confirm tools, voice, and search in the app you actually ship. Not measured here.<\/p><\/td>\n<\/tr>\n

Cost at volume<\/p><\/td>\n

Split<\/p><\/td>\n

Chat matched. Repo $0.010 vs $0.010. Cached agent $0.021 vs $0.021.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\nHow to read benchmarks<\/strong>\n

Leaderboards mix harnesses, tool settings, and effort modes. When we do not have a primary table for a pair, we do not invent one. Re-test on your workload.<\/p>\n<\/div>\n\n

\n\n

Side-by-side test (i10X pack, 2026-10-12)<\/h2>\n

Same three prompts, scored 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25). Writing, coding, and false premise only. No invented extra scores.<\/p>\n\n

Test 1: Client email rewrite<\/h3>\n

Task:<\/strong> Keep every fact. Warmer. Under 120 words.<\/p>\n

Claude Haiku 5.5 (excerpt, sanitized):<\/strong> Complete, polite “Hi there,” all facts, under 120 words, no greeting fluff.<\/p>\n

GPT-6 Luna (excerpt, sanitized):<\/strong> Same facts in a short note, no name slot, under 120 words.<\/p>\n

Edge:<\/strong> Split. A\/B on brand voice. Neither is allowed to drop Q3, Tuesday, Friday, Wednesday, finance, or Acme.<\/p>\n\n

Test 2: Empty-list average bug<\/h3>\n

Named ZeroDivisionError. None vs ValueError. Guard must come before the divide. Named ZeroDivisionError. Short ValueError guard. No extra options. Claude Haiku 5.5.<\/strong><\/p>\n\n

Test 3: False premise (Moon cheese)<\/h3>\n

Strong geology refuse, then Earth-realistic protein routes and lunar ice as support, not cheese. Refuse in two sentences. Suggests Earth beans and tofu. Thin as a plan. Claude Haiku 5.5.<\/strong><\/p>\n\n\n\n\n\n\n\n\n

Prompt type<\/p><\/th>\n

Claude Haiku 5.5<\/p><\/th>\n

GPT-6 Luna<\/p><\/th>\n

Note<\/p><\/th>\n<\/tr>\n

Client email rewrite<\/p><\/td>\n

23\/25<\/p><\/td>\n

23\/25<\/p><\/td>\n

Complete, polite “Hi there,” all facts, under 120 words, no greeting fluff.<\/p><\/td>\n<\/tr>\n

Bug explain + minimal fix<\/p><\/td>\n

24\/25<\/p><\/td>\n

23\/25<\/p><\/td>\n

Named ZeroDivisionError. None vs ValueError. Guard must come before the divide.<\/p><\/td>\n<\/tr>\n

Logic + false premise<\/p><\/td>\n

24\/25<\/p><\/td>\n

20\/25<\/p><\/td>\n

Strong geology refuse, then Earth-realistic protein routes and lunar ice as support, not cheese.<\/p><\/td>\n<\/tr>\n

Total<\/strong><\/p><\/td>\n

71\/75<\/strong><\/p><\/td>\n

66\/75<\/strong><\/p><\/td>\n

Claude Haiku 5.5 won this three-prompt pack (71\/75 vs 66\/75). That does not erase the other model’s cost or context edge. Route the next step.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Claude Haiku 5.5 won this three-prompt pack (71\/75 vs 66\/75). That does not erase the other model’s cost or context edge. Route the next step.<\/p>\n\n

\n\n

Ecosystem and where you run them<\/h2>\n