Four Frontier Models in Three Weeks: Opus 5.5, GPT-6 Astra, Sol and Luna, Grok 4.7 — What Each Is For, and What It Costs in Rupees

In twenty days, four frontier models shipped: GPT-6 Astra on 3 September, Grok 4.7 on 21 September, and Claude Opus 5.5 together with GPT-6 Sol and GPT-6 Luna on 22 September. If you run a business rather than a lab, the useful question is not which one is smartest. It is which one you should put behind which workflow, and what that decision does to a monthly bill. This piece answers that with published prices, published benchmarks, and the same job costed across every model in rupees.

Prices and specifications as published on 23 September 2026, at list API rates, converted at approximately ₹95.6 to the dollar. Benchmark figures are as published by the model vendors or in their launch coverage, which means most of them are self-reported — they are useful for direction and unreliable as a ranking. Model versions change frequently; check the vendor's current pricing page before you budget.

1. What actually launched

Two more things worth knowing for context. Google's line has not moved to a new frontier tier — Gemini 3.5 Pro never shipped, Gemini 4 is reported to be in pre-training with no date, and the live options are Pro at the top, 3.6 Flash in the middle and Flash-Lite for volume. And India has its own sovereign models: Sarvam-30B and Sarvam-105B, built under the IndiaAI Mission and unveiled at the India AI Impact Summit in February 2026, with 32K and 128K context windows respectively and a design focus on Indian languages and voice.

2. The price list, in one table

Three observations a finance person should take from that table. Opus 5.5 is priced at $4/$20 — a 20% cut on its predecessor, and Anthropic claims a 40% reduction in cost on typical workloads with output around 30% faster. Its cache read price of $0.20 is 5% of its input price, the most aggressive caching discount on the list. And Astra's headline rate is 2.5 times the previous generation's flagship rate — the frontier tier got more expensive even as the tier below it got cheaper.

3. The same job, costed in rupees

List prices per million tokens are hard to feel. So here is one identical task — a 50,000-token input (roughly a long contract, a set of financial statements, or a stack of emails) producing a 2,000-token answer — priced on each model, at ₹95.6 to the dollar.

The spread on one identical job is a hundredfold. For a firm processing two thousand documents a month, the same workflow is ₹1,147 or ₹1.15 lakh depending only on which model you pointed it at. That is not a rounding difference; it is a staffing decision.

And the lever most teams miss

Caching. If your prompt repeats a large fixed block — a policy document, a chart of accounts, a code base, a standard instruction set — the repeated part can be billed at the cache read rate instead of the input rate. On Opus 5.5 that is $0.20 against $4.00, a 95% reduction on the repeated portion. On Astra it is $1 against $10, a 90% reduction.

Rework the 50,000-token task with 45,000 tokens of it cached and repeated:

A team on a frontier model with good caching can pay less than a team on a mid-tier model without it. Before anyone argues about which model to buy, find out whether your prompts are structured so the fixed part can be cached.

4. What the benchmarks actually say

Anthropic published these for Opus 5.5:

And for Grok 4.7, the gains reported against Grok 4.6, with the honest note that it does not lead everywhere:

OpenAI's claim for the new efficient tier is that Sol makes about half as many mistakes as its predecessor and reaches Astra-level reliability at much lower cost, and that both Sol and Luna handle tasks substantially better than Anthropic's Opus and Fable models. Anthropic's claim is that Opus 5.5 performs at the level of Fable 5.1 on most work at 40% less cost. Both cannot be fully true at once, and neither is independently audited. Treat vendor benchmarks the way you would treat a company's own earnings presentation: informative about direction, not a substitute for your own testing.

One technical note with a governance consequence: Astra uses a reasoning technique described as recurrent depth or looped transformers, which improves efficiency but obscures some or all of the model's chain of thought. If your compliance posture depends on being able to inspect the reasoning behind an output, that is a material design fact, not a detail.

5. The practical routing table

Here is how I would actually assign work, given the prices and the published strengths:

6. How to choose without guessing

1. Write down the job, not the model. "Extract 12 fields from a GST invoice, 4,000 a month" is a specification you can test. "Which AI is best" is not.
2. Build a 50-case test set from your own work, with the right answers written by hand. Every vendor benchmark above is a proxy for someone else's job.
3. Start at the cheapest tier that could plausibly work and measure the failure rate. The question is never "is the big model better" — it always is. The question is whether the gap is worth 100x.
4. Price the failure, not just the token. If a wrong extraction costs an hour to catch and fix, a model that fails 4% of the time on 2,000 tasks costs you 80 hours — which dwarfs any API bill on this page.
5. Cache before you upgrade. Restructure prompts so the fixed context is cacheable; re-measure; then decide whether you still need a bigger model.
6. Re-test quarterly. Four frontier models landed in twenty days. Any routing decision you make today has a shelf life measured in months.

Frequently Asked Questions

What is Claude Opus 5.5 and when was it released? Anthropic's flagship model for reasoning, coding and long-horizon agent work, released on 22 September 2026 as the first model of the 5.5 family. It is priced at $4 per million input tokens and $20 per million output tokens, with cache reads at $0.20, and Anthropic reports it performing at Fable 5.1's level on most work while costing about 40% less than Opus 5 on typical workloads.

Is there a Fable 5.5? No. Anthropic's Fable line is at 5.1, priced at $10 per million input and $50 per million output tokens. The 5.5 family began with Opus 5.5 on 22 September 2026, with Sonnet 5.5 and Haiku 5.5 signalled for the following weeks.

What are GPT-6 Sol and GPT-6 Luna? OpenAI's efficient tiers of the GPT-6 generation, both released on 22 September 2026. Sol is for demanding work including coding, at $2 per million input and $10 per million output tokens. Luna is for high-volume, clearly-defined jobs such as summarising and extraction, at $0.10 and $0.50 — roughly one-hundredth of Astra's rate on a typical task.

What is the GPT-6 Astra API price? $10 per million input tokens and $50 per million output tokens on the standard API, with cached input at $1. Its context window is about 1,050,000 tokens with a maximum output of 128,000, and prompts above 272K input tokens are priced at a multiple of the standard rate.

How much does Grok 4.7 cost? $2 per million input tokens, $0.50 cached, and $6 per million output tokens — the same price as Grok 4.6 — with a 500K-token context window. It was released on 21 September 2026.

Which AI model is cheapest for high-volume document work? Of the models launched this month, GPT-6 Luna at $0.10/$0.50 per million tokens — about ₹0.57 for a 50,000-token input producing a 2,000-token answer. Gemini 3.5 Flash-Lite at $0.30/$2.50 is the next option. For bounded, checkable work the saving over a frontier model is roughly a hundredfold.

Which model is best for coding in 2026? On published benchmarks, Claude Opus 5.5 leads the coding set — 66.4% on Terminal-Bench 4.0 and 57.8% on CursorBench 4.0 — with Grok 4.7 at 46.3% on CursorBench for a third less money. These are vendor-reported figures, so test both on your own repository before committing.

Which model has the largest context window? GPT-6 Astra, at about 1,050,000 tokens, ahead of Grok 4.7's 500K. That matters only for genuinely single-pass enormous inputs; for most work, retrieval plus a cheaper model is far less expensive than paying frontier rates for a million tokens.

How much can prompt caching save? On the repeated portion of a prompt, roughly 95% on Claude Opus 5.5 ($0.20 against $4.00) and 90% on GPT-6 Astra ($1 against $10). On a task with 45,000 of 50,000 tokens cached, total cost falls about 71% on Opus 5.5, 68% on Astra and 60% on Grok 4.7 — often more than switching model tiers would save.

Are there Indian AI models worth considering? Yes — Sarvam-30B and Sarvam-105B, built under the IndiaAI Mission and unveiled at the India AI Impact Summit in February 2026, with 32K and 128K context windows and a design focus on Indian languages and voice. For Indian-language or voice-first customer workloads they are a serious option the global leaderboards do not measure.

Sources

1. Claude Opus 5.5: Anthropic, "Introducing Claude Opus 5.5" (release date, $4/$20 pricing, $0.20 cache reads and $5 cache writes, Terminal-Bench 4.0 66.4%, CursorBench 4.0 57.8%, FrontierCode v1.1 54.4%, OSWorld 2.0 81.8%, Humanity's Last Exam 67.7%, GDPval-AA v2.1 1846 Elo, availability); MacRumors and 9to5Mac coverage of 22 September 2026 for the 40% cost and 30% speed claims and the Sonnet/Haiku 5.5 signal.
2. Claude Fable 5.1 pricing ($10/$50, cache reads $0.25, Batch API at half): Anthropic's Fable 5.1 and Mythos 5.1 announcement and Firecrawl's measured comparison.
3. GPT-6 Astra: OpenAI's GPT-6 Astra page and system card; CNBC (3 September 2026) and Al Jazeera on the rollout and the recurrent-depth reasoning technique; Yotta Labs, Layer3Labs and MindStudio for the $10/$50 API rates, $1 cached input, 1,050,000-token context and 128K maximum output.
4. GPT-6 Sol and Luna: TechCrunch, "OpenAI launches GPT-6 Sol and Luna" (22 September 2026) and MacRumors, for pricing, positioning, the half-the-mistakes claim and availability.
5. Grok 4.7: MarkTechPost and llm-stats.com for the 21 September 2026 release, $2/$0.50/$6 pricing and 500K context; reported benchmark movements on CursorBench 4.0, EEBench and the Harvey Legal Agent benchmark, and where Fable 5.1 Max and GPT-5.6 Sol Max lead.
6. Google: CloudZero and BenchLM for current Gemini pricing lanes; reporting that Gemini 3.5 Pro did not ship and that Gemini 4 is in pre-training with no announced date.
7. Sarvam: Sarvam AI and IndiaAI Mission material on the sovereign LLM, and reporting of the Sarvam-30B and Sarvam-105B launch at the India AI Impact Summit, February 2026.
8. Rupee conversion at approximately ₹95.6 to the dollar — the close of 22 September 2026.

Benchmark figures above are vendor-reported unless stated otherwise and are not independently audited. Model prices change often and regional and enterprise rates differ from list. Nothing here is a recommendation to buy a particular service, and no vendor has any relationship with this site.