Claude Haiku 5.5 vs GPT-6.1 Sol: Why Haiku 5.5 Is 20x Cheaper for Coding Agents

Quick answer (checked 18 October 2026): On list price, Claude Haiku 5.5 costs $0.10 per million input tokens and $0.50 per million output tokens, while GPT-6.1 Sol costs $2 and $10. That's exactly 20x cheaper on both input and output, and 10x cheaper on cached input ($0.01 vs $0.10). The gap is real for short, high-volume agent calls. It shrinks to about 4x once a Haiku prompt goes over 100,000 tokens, because Haiku then bills at $0.50 / $2.50. And the two models aren't the same class: Sol is OpenAI's mid-tier model for complex work, while Haiku's real price twin is GPT-6 Luna, which has the same list price.

Here's the moment that made me write this. I was looking at the cost breakdown of a coding-agent setup: one strong model planning the work, and a swarm of small calls underneath it doing the grunt work. That means reading files, summarizing test output, classifying errors and writing commit messages. The grunt work was most of the calls, and most of the bill.

Then two pricing pages landed within a week of each other. Anthropic released Haiku 5.5 on 7 October. OpenAI had just shipped GPT-6.1 Sol at DevDay on 30 September. People started posting "Haiku is 20x cheaper than Sol!" as if that settled which one to use.

The 20x number is correct. But it answers a narrower question than most people think. Let me walk through what the pricing pages actually say, where the catches are, and how I'd decide between them. One honesty note first: I haven't run a long benchmark of my own on either model. Every price and score below comes from the official pages linked in the Sources section, and the cost examples are arithmetic you can redo yourself.

The prices, side by side

Here are the standard API list prices per million tokens, from Anthropic's and OpenAI's own pages:

So the headline holds: $2 ÷ $0.10 = 20, and $10 ÷ $0.50 = 20. Cached input is 10x apart ($0.10 ÷ $0.01).

Two more pricing rules matter for agents:

• Haiku 5.5 is priced by prompt length. Anthropic says a request whose prompt is over 100,000 tokens pays the higher prices, counting every input token, including cache reads and writes. Each request is priced on its own.
• GPT-6.1 Sol has a long-prompt rule too, but it starts much later. OpenAI's model page says prompts over 272K input tokens pay 2x on input and cache, and 1.5x on output, for the full request.

Both providers also offer a 50% batch discount for work that doesn't need an immediate answer.

Where the 20x is real

The 20x gap shows up best in exactly the work Anthropic built Haiku for. Its docs describe Haiku 5.5 as built for "high-volume, latency-sensitive work such as classification, routing, extraction, and subagent tasks". In a coding agent, that's things like:

• Summarizing a long test log down to the failing tests
• Classifying an error ("flaky test", "type error", "missing dependency")
• Reading a file and answering one narrow question about it
• Writing commit messages and PR descriptions from a diff
• Compacting a long conversation into a short summary

These calls are short, there are a lot of them, and they rarely need deep reasoning.

Worked example 1: a busy subagent

Say a subagent makes 500 calls a day, each with 40,000 input tokens and 4,000 output tokens. That's 20 million input and 2 million output tokens a day.

That's the 20x, plain and simple: about $90 a month versus about $1,800.

Worked example 2: the same subagent with caching

Agents resend a lot of the same context (system prompt, tool definitions, project instructions). Suppose 30,000 of the 40,000 input tokens on each call are a cache hit. I'm leaving out the one-time cache writes to keep the maths readable.

Still about 19x. Notice something else: in both cases output is now the biggest line. That's normal for agents, and it's why keeping answers short saves so much money. I cover that in how to cut AI coding agent costs.

Where the 20x shrinks: long prompts

Now take a different job: feeding a model a big slice of a codebase in one go, say 150,000 input tokens and 4,000 output tokens, 100 times a day.

At 150K tokens, Haiku is over its 100K threshold, so the higher prices apply to the whole request. Sol is still under its 272K threshold, so it pays standard rates.

Now Haiku is 4x cheaper, not 20x. Still cheaper, but the gap isn't what the headline suggests.

The practical lesson: if you use Haiku 5.5 for subagents, keep each call's prompt under 100K tokens. Send the subagent the three relevant files, not the whole repository. That one habit keeps you in the cheap tier.

The tokenizer catch

Price per token only tells half the story. The other half is how many tokens your text turns into.

Anthropic's docs say Haiku 5.5 uses the same newer tokenizer as Claude 4.7 and later models, so the same text counts as about 30% more tokens than on Haiku 4.5. The exact increase depends on the content.

OpenAI's models use a different tokenizer, so the same prompt won't be the same token count on both providers. I haven't seen an official like-for-like comparison, and I'm not going to invent one. That's why the only fair comparison is cost per finished task: run the same 50 real tasks through each model, read the token counts from each API's usage fields, and compare the bills.

Two more Haiku 5.5 details that change token counts in agent loops, both from Anthropic's "What's new" page:

• Earlier thinking blocks stay in context and count as input tokens. Haiku 4.5 kept only the latest turn's. Long multi-turn loops therefore carry more input than you might expect. Anthropic offers thinking block clearing to trim them.
• Thinking tokens count toward max_tokens. If you set a small limit, a response can stop after the thinking block and before any text. Give it some headroom.

They're not the same class of model

This is the part the "20x cheaper" posts skip.

GPT-6.1 Sol is the middle of OpenAI's GPT-6 family, priced at one-fifth of the flagship Astra. OpenAI's announcement positions it for complex coding and professional work. It reports 75.2% on DeepSWE v1.1 at high effort, up from GPT-6 Sol's 68.8%. Haiku 5.5 is Anthropic's small model. Anthropic is direct about its limits: it says Sonnet 5.5 and Opus 5.5 "remain better choices for complex agentic coding".

So "Haiku vs Sol" is a bit like comparing a fast junior developer with a senior one. The junior is far cheaper per hour, and great for a lot of work. You still wouldn't hand them the architecture redesign.

Haiku's real price twin is GPT-6 Luna: same $0.10 input, $0.01 cached input and $0.50 output. On that matchup, Anthropic's own launch table shows a clear lead for Haiku on agentic tasks:

Two cautions. These are Anthropic's numbers, published on its own launch page, and the page doesn't say which effort setting each score used. And there's no benchmark in the official posts that both Haiku 5.5 and GPT-6.1 Sol report, so I can't give you a fair head-to-head score between those two. Anyone who shows you one should tell you where it came from.

How I'd split the work

Here's the setup I'd start with for a coding agent, based on what each provider says the models are for:

1. Planner / main agent: a strong model, such as GPT-6.1 Sol, Claude Sonnet 5.5 or Opus 5.5. This is where mistakes are expensive, so the higher price per token pays for itself in fewer redo loops.
2. Subagents for narrow jobs: Haiku 5.5 (or GPT-6 Luna). Log summaries, file questions, classification, commit messages. Anthropic specifically says Haiku 5.5 "pairs well with Opus 5.5 and Sonnet 5.5 as a subagent on coding work".
3. Background jobs: anything that can wait, such as nightly repository summaries or bulk labelling, goes through the Batch API for another 50% off.

If you use Claude Code, subagents can run on a cheaper model than your main session, which keeps their noisy output out of your main context too. I wrote about setting that up across projects in Claude Code across multiple repositories.

Step by step: test this on your own work

Don't trust my maths or anyone's headline. This takes an afternoon:

1. Pick 30–50 real tasks from your agent logs. Use the boring ones (summaries, classifications), because that's where the volume is.
2. Run each task on both models with the same prompt. On Haiku 5.5, start at a low or medium effort; it's the first Haiku with adjustable effort (Low through Max).
3. Log the usage fields each API returns: input, cached and output tokens. Don't estimate from word counts; the tokenizers differ.
4. Grade the results pass/fail with a simple rule you decide before you start, such as "did the summary name every failing test?"
5. Compute cost per passed task, not cost per call. A cheap model that fails 40% of the time can end up costing more once you count the retries.
6. Check prompt sizes. Count how many Haiku calls went over 100K tokens. If it's more than a handful, trim what you send.
7. Keep the cheaper model only where its pass rate is acceptable.

Migration gotchas if you're coming from Haiku 4.5

Anthropic warns that code written for Haiku 4.5 can break on 5.5. The breaking changes in its docs:

• temperature, top_p and top_k can return an error. Remove them.
• Manual extended thinking with budget_tokens returns an error. Use adaptive thinking with the effort parameter instead.
• Assistant message prefill returns an error. End messages with a user turn.
• Responses can begin with thinking blocks. Select content blocks by type, not by position, or you'll read the wrong block as the answer.
• Safety classifiers can decline a request with stop_reason: "refusal", so handle that in your client.

One nice change: the minimum cacheable prompt is now 512 tokens, down from 4,096 on Haiku 4.5. Short system prompts can now be cached.

Common mistakes to avoid

• Comparing price per token instead of cost per finished task. Different tokenizers and different failure rates make per-token prices misleading on their own.
• Sending whole repositories to Haiku. Going over 100K tokens makes every token in that request 5x more expensive.
• Using a small model as the planner. You save on tokens and lose it all on wrong plans and retries.
• Forgetting that output dominates. In both examples above, output was the biggest cost. Ask for short, structured answers.
• Reading launch benchmarks as independent tests. They're the vendor's own numbers. Useful, but check them on your tasks.
• Ignoring the batch discount. Anything that doesn't need an answer in seconds can run at half price.

Final thoughts

The "20x cheaper" line is true on the price list, and for short, high-volume subagent calls it's roughly what you'll see on the bill. It's not a reason to swap your main coding model for Haiku. The setup that makes sense is a strong model doing the thinking and a cheap model doing the volume, with prompts kept under Haiku's 100K line.

If you're choosing between small models, compare Haiku 5.5 with GPT-6 Luna, which costs the same. If you're choosing a main coding model, compare Sol with Sonnet 5.5 and Opus 5.5. Either way, an afternoon of testing on your own tasks will tell you more than any comparison table, this one included.

Frequently asked questions

Is Claude Haiku 5.5 really 20x cheaper than GPT-6.1 Sol?

On standard list prices, yes: $0.10 vs $2 per million input tokens and $0.50 vs $10 per million output tokens. For prompts over 100,000 tokens, Haiku 5.5 bills at $0.50 / $2.50, so it's about 4x cheaper instead.

How much does Claude Haiku 5.5 cost?

$0.10 per million input tokens, $0.01 per million cached input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens. Over 100,000 tokens: $0.50 input, $0.05 cached input and $2.50 output. The Batch API halves these prices.

How much does GPT-6.1 Sol cost?

$2 per million input tokens, $0.10 per million cached input tokens and $10 per million output tokens. Prompts over 272,000 input tokens pay 2x on input and 1.5x on output.

Is Haiku 5.5 as good as GPT-6.1 Sol for coding?

They're different classes. Anthropic says Sonnet 5.5 and Opus 5.5 remain better than Haiku 5.5 for complex agentic coding. Haiku 5.5 is built for high-volume subagent work such as classification, extraction and summaries.

What's the closest OpenAI model to Claude Haiku 5.5?

GPT-6 Luna. It has the same list price ($0.10 input, $0.01 cached, $0.50 output). In Anthropic's launch table, Haiku 5.5 scores 39.2% on Terminal-Bench 4.0 against Luna's 16.4%.

What is Claude Haiku 5.5's context window?

1 million tokens, with up to 128,000 output tokens, according to Anthropic's docs.

Why does my Haiku 5.5 bill look higher than expected?

Three common reasons: prompts over 100,000 tokens (higher pricing tier), the newer tokenizer (about 30% more tokens than Haiku 4.5 for the same text), and earlier thinking blocks staying in context as input tokens in multi-turn loops.

Sources

• Anthropic: Introducing Claude Haiku 5.5 (7 October 2026)
• Claude Platform Docs: What's new in Claude Haiku 5.5
• Claude Platform Docs: Pricing (Haiku 5.5 long-context and batch pricing)
• OpenAI: GPT-6.1 Sol model page and GPT-6 Luna model page
• OpenAI: Introducing GPT-6.1 Sol and the developer forum announcement

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