OpenAI’s GPT-6 generation has landed on unified API catalogs, and the tier structure is now clear: Astra (flagship), Sol (workhorse), 6.1 Sol (the refreshed workhorse), and Luna (speed tier). If you are budgeting for production traffic, the spread inside one family is enormous — $0.10 to $10.00 per million input tokens, a 100× range — and picking the wrong tier is the most expensive one-line mistake you can make.
This is a spec-and-pricing analysis, not a synthetic benchmark. Every number below comes from the live MeshTok catalog (src/data/models.ts in our repo, mirrored on the models page) and is reproducible with a calculator. Figures reflect the catalog on 2026-10-11.
The GPT-6 lineup at a glance
All four models are available today through the MeshTok unified API at https://meshtok.com/v1.
| Model id | Context | Input $/M | Output $/M | Capability tags |
|---|---|---|---|---|
openai/gpt-6-astra | 400,000 | $10.00 | $50.00 | reasoning, coding, tools, vision, flagship |
openai/gpt-6-sol | 1,000,000 | $2.00 | $10.00 | reasoning, coding, tools |
openai/gpt-6.1-sol | 1,000,000 | $2.00 | $10.00 | reasoning, coding, tools |
openai/gpt-6-luna | 1,000,000 | $0.10 | $0.50 | fast, tools |
Three structural observations before the per-tier breakdown:
- The family keeps the 1M-token window below the flagship. Astra caps at 400K context; Sol and Luna both carry the full million-token window. If your workload is long-document RAG or whole-repo analysis, the flagship is not automatically the right pick — it has the smallest context in the family.
- Luna is priced like last year’s nano tier. At $0.10/$0.50 per million tokens, Luna undercuts nearly every “fast tier” rival in the catalog, including models whose only selling point is price.
- Sol and 6.1 Sol cost the same. The 6.1 refresh (better reasoning and coding over 6 Sol per the vendor’s positioning) is a free upgrade at equal price — there is no reason to start new workloads on 6 Sol unless you need the older snapshot for reproducibility.
Tier by tier
GPT-6 Astra — the flagship ($10.00 / $50.00)
Astra is positioned for demanding end-to-end work: advanced analysis, software engineering, deep research, and document creation where computer use and long-running autonomy decide outcomes. At $60.00 per million mixed tokens (1M in + 1M out), it costs 5× GPT-5.5 ($5.00/$30.00) and 2× Claude Opus 5.5 ($4.00/$20.00). That is flagship pricing for flagship problems: agentic loops that run for hours, hard research synthesis, and migration-scale refactors. It is not a model you route default traffic to.
GPT-6 Sol / 6.1 Sol — the default pick ($2.00 / $10.00)
Sol carries most of Astra’s reasoning and coding strength at a fifth of the price: $12.00 per million mixed tokens. For comparison, that lands it below Claude Sonnet 5.5’s predecessor pricing and roughly on par with Claude Sonnet 5.5 ($2.00/$10.00) — with a larger context window (1M vs Sonnet 5.5’s 1M, equal) and no vision tag. If you are picking one GPT-6 model for production traffic, this is it. Prefer gpt-6.1-sol for new builds; keep gpt-6-sol only if you need to pin a snapshot.
GPT-6 Luna — the speed tier ($0.10 / $0.50)
Luna is the chat/routing/extraction workhorse: $0.60 per million mixed tokens, a million-token window, and the fast tag. The interesting comparison is not inside the GPT-6 family but across vendors — at $0.10 input, Luna matches Claude Haiku 5.5 ($0.10/$0.50) almost exactly and undercuts DeepSeek V4 Flash ($0.15/$0.45) on input while costing slightly more on output. This tier is a coin flip on specs; pick on latency from your own traffic.
What a realistic workload actually costs
Budget math for a typical production mix (1M input + 0.25M output tokens per day, 30 days):
| Model | Daily cost | Monthly cost |
|---|---|---|
| GPT-6 Astra | $10.00 + $12.50 = $22.50 | $675.00 |
| GPT-6 Sol (6.1) | $2.00 + $2.50 = $4.50 | $135.00 |
| GPT-6 Luna | $0.10 + $0.125 = $0.225 | $6.75 |
| Claude Opus 5.5 | $4.00 + $5.00 = $9.00 | $270.00 |
| DeepSeek V4.1 Flash | $0.15 + $0.15 = $0.30 | $9.00 |
The gap between Luna and Astra on an identical workload is 100×. Most pipelines should route 80–90% of calls to Sol or Luna and reserve Astra for the specific requests that genuinely need flagship depth.
Cheaper alternatives that cover the same ground
If you are not locked into OpenAI, the October 2026 catalog has strong substitutes at every GPT-6 tier:
- Instead of Astra ($10.00/$50.00): Claude Opus 5.5 ($4.00/$20.00) is half the price with the same agentic-coding positioning and a 1M window; Claude Fable 5.1 ($10.00/$50.00) matches Astra’s price exactly if you want Anthropic’s long-running-agent specialist instead.
- Instead of Sol ($2.00/$10.00): DeepSeek V4 Pro ($0.67/$1.34) is a flagship-tagged reasoning model at a third of Sol’s input price; Grok 4.7 ($1.60/$4.80) undercuts Sol with a 500K window and coding-agent focus.
- Instead of Luna ($0.10/$0.50): GLM-5.3-Flash ($0.12/$0.42) is the cheapest multimodal model in the catalog right now; MiMo V2.6 Flash ($0.15/$0.30) has the cheapest output price of any 1M-context model.
All of them speak the same OpenAI-compatible dialect — switching is a one-line model string change, no new SDK, no new auth. That is the whole point of a unified gateway: tier-hopping to control cost should be a config change, not a migration.
How to call GPT-6 through MeshTok
from openai import OpenAI
client = OpenAI(
base_url="https://meshtok.com/v1",
api_key="sk-your-MeshTok-key",
)
resp = client.chat.completions.create(
model="openai/gpt-6.1-sol", # or gpt-6-astra / gpt-6-sol / gpt-6-luna
messages=[{"role": "user", "content": "Ship it."}],
)Prices in this article reflect the MeshTok catalog on 2026-10-11. The live source of truth is always the pricing page and each model page, which are regenerated from the catalog on every deploy.