For chatting with a neural network, choosing a model is a matter of taste. For a bot that processes ten thousand messages a day, it's a matter of budget: the difference between an expensive and a cheap model is dozens of times over, while quality on simple tasks is almost identical. In October 2026 the budget segment got an update: Anthropic released Claude Haiku 5.5 with tokens 10 times cheaper than the larger models, and it competes directly with GPT-6 Luna from OpenAI and Gemini 3.5 Flash from Google. We break down which of the three to pick for which task.
Why "small" models are needed at all
Fast models are scaled-down versions of the flagships: they respond in fractions of a second, cost pennies, and still handle 80% of real-world tasks where deep reasoning isn't needed. Typical scenarios:
- Telegram bots and chat support — answers to common questions, collecting requests, routing inquiries.
- Text stream processing — classifying emails and reviews, extracting data from documents, moderation.
- Translations and summaries — at scale and fast.
- Draft work in agents — a cheap model does the routine steps, and an expensive one kicks in for the hard ones. We covered how this works in the guide to building an agent.
- Pet projects — when you want to experiment without watching the bill.
For such tasks a flagship is overkill: you're paying for capabilities you don't use.
The three models compared
Claude Haiku 5.5
The big news of the segment. Anthropic gave figures relative to the previous version, Haiku 4.5:
- 39% success on hard coding tasks versus zero for its predecessor;
- 72% on computer-use tasks versus 16%;
- on general benchmarks — higher than GPT-6 Luna;
- token cost reduced 10 times.
In practice this means Haiku is no longer a "simple-only" model. It confidently writes code for small tasks, follows long instructions, and can work in agentic scenarios where the model chooses tools itself. The text style is signature Claude: natural Russian, without boilerplate phrasing.
The weak spot is deep reasoning and unconventional tasks: here Haiku is noticeably behind Sonnet 5.5.
GPT-6 Luna
The fast model in the GPT-6 lineup alongside Sol and Astra. OpenAI positions Luna as the workhorse for high-volume requests, and the company's official guide directly describes which tasks to give to Luna and which to the larger models.
Its strengths are the ecosystem and predictability: structured output, function calling, compatibility with a huge number of libraries and ready-made solutions. If your bot is already written against the OpenAI API, switching to Luna is a one-line change.
On quality, Luna trails Haiku 5.5 on general benchmarks, but the gap is small and depends on the task. In Russian both models are good; Claude is slightly more natural in style.
Gemini 3.5 Flash
The fastest and cheapest model of the trio, working by default in Google Search. Its main trump cards are a million-token context and multimodality: Flash can take a long document, an image, or a video as input and process them in seconds.
For purely text tasks Flash is slightly behind Haiku 5.5 in reasoning quality, but it wins where volume matters: summarizing long documents, analyzing video, processing streams with images. The free API tier through AI Studio is the most generous of the three, which makes Flash the best choice for zero-budget pet projects.
Comparison table
| Criterion | Claude Haiku 5.5 | GPT-6 Luna | Gemini 3.5 Flash |
|---|---|---|---|
| Quality on simple tasks | ★★★★★ | ★★★★☆ | ★★★★☆ |
| Code | ★★★★☆ | ★★★★☆ | ★★★☆☆ |
| Agentic tasks | ★★★★★ | ★★★★☆ | ★★★☆☆ |
| Russian language | ★★★★★ | ★★★★☆ | ★★★★☆ |
| Speed | ★★★★☆ | ★★★★☆ | ★★★★★ |
| Context | 200K | 256K | 1M |
| Multimodality | text, images | text, images | text, images, video, audio |
| Price per token | low | low | lowest |
| Free API tier | no | limited | generous |
| Ecosystem and compatibility | ★★★★☆ | ★★★★★ | ★★★★☆ |
Model specs and current prices are in the catalog; you can calculate a budget for your load in the cost calculator, and the number of tokens in your requests in the token counter.
What this costs in practice
Let's take a typical support bot: 10 thousand dialogs a day, averaging 1500 input tokens (dialog history plus instructions) and 300 output tokens. That's 15 million input and 3 million output tokens a day, about 540 million tokens a month.
On fast models such a bot costs tens of dollars a month — the order of magnitude is the same for all three, with differences within a factor of two. On a mid-tier model the same bot would cost hundreds of dollars, and on a flagship, thousands. That's exactly why choosing between Haiku, Luna, and Flash matters less than the decision to use the fast tier at all.
Where the difference becomes noticeable is long context. If each request carries 50–100 thousand tokens (for example, a bot answering based on a large document), Flash with its input-token price and million-token context wins by a wide margin.
What to choose for each task
Telegram bot or support in Russian. Claude Haiku 5.5: the best answer style in Russian and good instruction-following, which is critical for bots with a rigid scenario.
You already have code built for OpenAI. GPT-6 Luna: minimal rework, predictable structured output, a huge ecosystem.
Long documents, images, video. Gemini 3.5 Flash: a million-token context and multimodality at no extra charge.
An agent with tools. Claude Haiku 5.5: 72% on computer use — the best result among fast models.
Zero budget. Gemini 3.5 Flash through the free AI Studio tier.
Confidential data. None of the three: look toward open models — compact versions of Qwen or Llama on your own server. We explain how to run them in the local setup guide.
How to combine with expensive models
The most effective scheme in 2026 is a cascade. The fast model handles all requests and decides for itself whether it can cope; it passes the hard cases to the larger model. In a support bot it looks like this: Haiku answers 90% of questions, and 10% of non-standard ones go to Sonnet 5.5 or GPT-6.1 Sol. The final cost is close to that of the fast model, while quality on hard questions is close to the flagship.
The second technique is splitting by stage. Classification and data extraction — the fast model; generating the final text for the client — the mid-tier one. That way you pay for quality only where the user sees it.
What to expect next
The budget segment updates faster than the flagship one: labs compete for high-volume workloads, and every two or three months a model comes out that makes the previous one obsolete. OpenAI's answer to Haiku 5.5 and the Flash update under Gemini 4 are a matter of the coming months. If you're building a product on a fast model, build in the ability to switch providers: the APIs of all three are structurally compatible, and switching takes hours, not weeks.
We write about every such release in our channel on the day it comes out — with figures and a comparison against competitors, so you can make the migration decision right away.