Most current frontier models now read about a million tokens — roughly 750,000 words — so window size no longer separates them much: GPT-6.1 Sol and GPT-6 Astra take 1.05M, and Claude Opus 5.5, Sonnet 5.5, Gemini 3.1 Pro and Gemini 3.8 Flash take 1M. What differs is recall deep into the document and cost per question. GPT-6.1 Sol is the best value at $2/$10. When recall matters most, GPT-6 Astra posts 96.3% on OpenAI's 8-needle MRCR test at 512K–1M tokens, at $10/$50.
The safest long documents default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GPT-6.1 Sol is the strongest answer here for long documents — pick it when quality of output matters more than the $2.00/1M/1M input you pay for it.
Gemini 3.8 Flash handles the same job for about 63% less per token. Start here and only move up if the output is not good enough.
GPT-6 Astra is the fastest of these for long documents — worth it when latency is what the reader notices, not the last few points of reasoning depth.
Current frontier models all hold about a million tokens; the GPT-6 family is slightly larger at 1.05M.
GPT-6 Astra has the strongest published long-context recall: 96.3% on 8-needle MRCR at 512K–1M tokens.
Every question over a full million-token document re-sends that context — prompt caching cuts the cost.
Use GPT-6.1 Sol or Claude Opus 5.5 for most long-document analysis.
Use GPT-6 Astra when a missed clause or figure deep in the document is expensive.
Use Gemini 3.8 Flash for cheaper long-document summaries and search.
Switch the scoring lens to see whether the long documents answer changes when cost, speed, or long-document depth leads the decision.
OpenAI / Balanced / Oct 10, 2026
Near-Astra capability at Sol pricing — OpenAI's value pick.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need it inside ordinary ChatGPT chats today, or your prompts regularly pass 272K tokens where the rate doubles.
Where the long documents recommendation shifts once you weigh price or latency differently.
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.
Anthropic's new coding default — top SWE-bench Pro score at a lower price than Opus 5.
Google's fast agentic workhorse — strong coding at Flash pricing.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GPT-6.1 SolOpenAI | $2.00/1M | $10.00/1M | $40 | 1.1M tokens | Balanced | 98 | 94 | 97 |
| GPT-6 AstraOpenAI | $10.00/1M | $50.00/1M | $200 | 1.1M tokens | Deliberate | 100 | 97 | 100 |
| Claude Opus 5.5Anthropic | $4.00/1M | $20.00/1M | $80 | 1M tokens | Deliberate | 100 | 97 | 99 |
| Gemini 3.8 FlashGoogle | $0.75/1M | $3.75/1M | $15 | 1M tokens | Very fast | 92 | 86 | 89 |
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 long documents, what it is genuinely good at, and where we would steer you away from it.
The default answer for long documents — 98/100 on the coding axis, and the model we would start with unless the price below rules it out.
OpenAI's September 29, 2026 DevDay release — a Sol-tier reasoning model that OpenAI says comes close to GPT-6 Astra on agentic coding, computer use and professional work at one fifth of Astra's price.
You need it inside ordinary ChatGPT chats today, or your prompts regularly pass 272K tokens where the rate doubles.
OpenAI's best value for serious work. GPT-6.1 Sol replaces both GPT-6 Sol and, for most teams, GPT-6 Astra: independent testing has it a point behind Astra at a fifth of the list price. Pick Astra only when the last point of capability is worth five times the bill.
Full pricing, benchmark table and release notes on the GPT-6.1 Sol page.
The fastest model in this shortlist for long documents. Pick it when turnaround is what your readers or users notice.
OpenAI's September 3, 2026 frontier release — the first GPT-6 model and OpenAI's answer to Claude Fable 5.1 two days earlier. State of the art on computer use (OSWorld 2.0 72.6% in ~47% less time than GPT-5.6 Sol), agentic coding (Terminal-Bench 4.0 57.9%), and frontier math (FrontierMath Tier 4 97.6%). $10/$50 per 1M tokens, 1.05M context, 128K output, knowledge cutoff April 30, 2026.
You are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak.
The new computer-use and agentic-coding ceiling, and OpenAI's first model priced like a Mythos-class Claude. Astra beats Claude Fable 5.1 on Terminal-Bench 4.0 (57.9% vs 55.8%), Terminal-Bench Science (64.6% vs 52.6%) and FrontierMath Tier 4 (97.6% vs 87.8%), and it is the only model with a credible OSWorld 2.0 result above 70%. It loses to Fable 5.1 on Humanity's Last Exam and on Artificial Analysis's index, costs five times GPT-5.6 Sol, and ships without a SWE-bench number. If your work is computer use, browser agents or math, it is the pick; for everyday coding at scale, Sol at $2/$10 remains the value default.
Full pricing, benchmark table and release notes on the GPT-6 Astra page.
Also worth a look for long documents, at 100/100 on the coding axis.
Anthropic's new coding default — top SWE-bench Pro score at a lower price than Opus 5. Full Claude Opus 5.5 review →
The value option for long documents: about 63% less per token than GPT-6.1 Sol, at 92/100 on coding. Worth starting here and moving up only if the output disappoints.
Google's fast agentic workhorse — strong coding at Flash pricing. Full Gemini 3.8 Flash review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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For long documents, GPT-6.1 Sol (OpenAI) is our pick. Near-Astra capability at Sol pricing — OpenAI's value pick. It costs $2/1M input and $10/1M output tokens, with a 1.05M-token context window — enough headroom for all but the largest long documents jobs. GPT-6 Astra is the closest alternative if it doesn't fit your setup.
Because the work it is built for overlaps closely with long documents: coding agents in Codex that need near-Astra quality at Sol pricing and computer-use and browser automation run at volume. OpenAI's best value for serious work. GPT-6.1 Sol replaces both GPT-6 Sol and, for most teams, GPT-6 Astra: independent testing has it a point behind Astra at a fifth of the list price. Pick Astra only when the last point of capability is worth five times the bill.
On a moderate month — 10M input and 2M output tokens — GPT-6.1 Sol runs about $40.00 at list price, with no batch or caching discounts applied, so treat that as a ceiling. Gemini 3.8 Flash is the cheaper route at roughly $15.00 for the same volume, if long documents is high-volume enough for price to lead the decision.
Prompts above 272K input tokens are billed at $4/$15. At launch it was available in ChatGPT Work and Codex, not in regular ChatGPT conversations. Avoid it if you need it inside ordinary ChatGPT chats today, or your prompts regularly pass 272K tokens where the rate doubles. None of that rules it out for long documents on its own — but if one of those limits maps onto how you actually work, take the alternative on this page seriously rather than defaulting to the top pick.
Gemini 3.8 Flash at $0.75/1M input is the budget option here. Google's fast agentic workhorse — strong coding at Flash pricing. Expect a quality step down on the hardest cases — the usual pattern is to route routine long documents volume to Gemini 3.8 Flash and keep GPT-6.1 Sol for the work where a wrong answer is expensive.
Gemini 3.8 Flash, rated very fast against GPT-6.1 Sol's balanced. Speed matters most for interactive and high-volume work; if your long documents runs in the background, the slower and more capable model is usually the better trade.