AI API prices in October 2026 span a hundredfold range for input tokens. At the top, GPT-6 Astra and Claude Fable 5.1 cost $10/$50 per million tokens. The mid-tier has settled at $2/$10, where Claude Sonnet 5.5, GPT-6 Sol and GPT-6.1 Sol all sit. At the bottom, Claude Haiku 5.5 and GPT-6 Luna cost $0.10/$0.50, and Mistral Small 3.1 is cheaper still on output at $0.10/$0.30. For most teams the best value is a two-tier setup: a $2/$10 model for work that matters and a $0.10 model for volume.
The safest this comparison default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Claude Haiku 5.5 is the strongest answer here for this comparison — pick it when quality of output matters more than the $0.10/1M/1M input you pay for it.
Mistral Small 3.1 handles the same job for about 33% less per token. Start here and only move up if the output is not good enough.
GPT-6 Luna is the fastest of these for this comparison — worth it when latency is what the reader notices, not the last few points of reasoning depth.
The $2/$10 mid-tier (Claude Sonnet 5.5, GPT-6 Sol, GPT-6.1 Sol) is where most production work should run.
Claude Haiku 5.5 and GPT-6 Luna at $0.10/$0.50 make high-volume work cheap — but both charge more above a prompt-length threshold.
Frontier models at $10/$50 cost five times the mid-tier; they pay off only on tasks where errors are expensive.
Choose Claude Haiku 5.5 or GPT-6 Luna for classification, extraction, routing and drafts at volume; keep prompts under 100K tokens on Haiku to stay on its low rate.
Choose Claude Sonnet 5.5 or GPT-6.1 Sol when output quality affects customers or code that ships.
Choose GPT-6 Astra or Claude Fable 5.1 only for the hardest reasoning, research or agent tasks, and measure cost per finished task.
Switch the scoring lens to see whether the this comparison answer changes when cost, speed, or long-document depth leads the decision.
Anthropic / Balanced / Oct 10, 2026
Near-Opus 5.5 quality on scoped work at half the price.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
The work is long, ambiguous and multi-step — Anthropic positions Opus 5.5 for exactly that — or you need the lowest possible price, where Haiku 5.5 costs a twentieth as much.
Where the this comparison recommendation shifts once you weigh price or latency differently.
Anthropic's budget model, now with adjustable reasoning, at $0.10/$0.50.
OpenAI's budget tier at half the old price.
Ultra-cheap multimodal model for massive-volume, low-complexity pipelines.
Near-Opus 5.5 quality on scoped work at half the price.
Near-Astra capability at Sol pricing — OpenAI's value pick.
OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| Claude Haiku 5.5Anthropic | $0.10/1M | $0.50/1M | $2.00 | 1M tokens | Very fast | 86 | 84 | 82 |
| GPT-6 LunaOpenAI | $0.10/1M | $0.50/1M | $2.00 | 1.1M tokens | Very fast | 84 | 82 | 80 |
| Mistral Small 3.1Mistral | $0.10/1M | $0.30/1M | $1.60 | 128k tokens | Very fast | 55 | 66 | 52 |
| Claude Sonnet 5.5Anthropic | $2.00/1M | $10.00/1M | $40 | 1M tokens | Fast | 98 | 98 | 95 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for this comparison, what it is genuinely good at, and where we would steer you away from it.
Ranked first here for this comparison: 86/100 on coding, with the widest margin of anything in this line-up.
Anthropic's October 7, 2026 low-cost model and the first Haiku with adjustable reasoning effort — thinking can be switched off for simple requests or raised to max, at $0.10/$0.50 per 1M under 100K input tokens.
Your prompts routinely exceed 100K tokens, where the price quintuples, or the task needs sustained reasoning that Sonnet 5.5 handles far better.
The cheapest way to put an Anthropic model in a high-volume pipeline. Keep requests under 100K tokens to stay on the low rate, and set effort per task — at low effort it is a fast classifier, at max it handles real tool-use work.
Full pricing, benchmark table and release notes on the Claude Haiku 5.5 page.
The fastest model in this shortlist for this comparison. Pick it when turnaround is what your readers or users notice.
OpenAI's September 22, 2026 low-cost reasoning model, replacing GPT-5.6 Luna at $0.10/$0.50 per 1M — half the predecessor's price, with the same 1.05M context.
You are choosing a budget model from scratch — Claude Haiku 5.5 costs the same and scores higher on Artificial Analysis's index.
A cheaper Luna rather than a smarter one. At $0.10/$0.50 it matches Claude Haiku 5.5 on price, but independent scoring puts Haiku 5.5 slightly ahead; choose Luna when you are already on the OpenAI stack.
Full pricing, benchmark table and release notes on the GPT-6 Luna page.
The cost-conscious pick for this comparison, about 33% less per token than Claude Haiku 5.5 than the top choice while holding 55/100 on coding.
Ultra-cheap multimodal model for massive-volume, low-complexity pipelines. Full Mistral Small 3.1 review →
The alternative to check next for this comparison — 98/100 on coding.
Near-Opus 5.5 quality on scoped work at half the price. Full Claude Sonnet 5.5 review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Among the current models we recommend, Mistral Small 3.1 has the lowest list price at $0.10/$0.30 per million tokens, but it is an older, smaller model. Claude Haiku 5.5 and GPT-6 Luna cost $0.10/$0.50 and are far more capable, so they are the better cheap defaults.
For a moderate workload of 10M input and 2M output tokens, list prices give roughly $2 a month on Claude Haiku 5.5 or GPT-6 Luna, $40 on Claude Sonnet 5.5 or GPT-6.1 Sol, $80 on Claude Opus 5.5, and $200 on GPT-6 Astra or Claude Fable 5.1. These are ceilings: caching and batch discounts lower them.
When quality failures are expensive, yes. For engineering, legal or strategy work, a better answer often saves more than the price difference. For routine volume, a $0.10 model is usually the better operational decision.
Because cost follows the tokens a model actually uses. A verbose model, or a reasoning model that writes long hidden thinking, can cost more per finished task than a pricier model that answers briefly. Gemini 3.8 Flash, for example, is fast and cheap per token but verbose.
Often. OpenAI cut GPT-5.6 prices in July and ran a GPT-5.6 Sol promotion from late August; Anthropic cut prices with Opus 5.5 and Sonnet 5.5. We check prices daily against the Vercel AI Gateway and note changes in our changelog.