GPT-6 Sol vs Luna vs Astra vs Claude Opus 5.5 vs Gemini 3.8 Flash: Which AI Model Should You Use? (September 2026)

TL;DR: For most production work in late September 2026 the choice comes down to three models: GPT-6 Sol ($2 / $10 per million tokens) and Claude Opus 5.5 ($4 / $20) for coding, agents and anything that needs judgement, and Gemini 3.8 Flash ($0.75 / $3.75 until 31 December) or GPT-6 Luna ($0.10 / $0.50) for high-volume tasks. Reserve GPT-6 Astra and Claude Fable 5.1 ($10 / $50) for the hardest problems where a wrong answer costs more than five times the token bill. Every figure in this guide was read from the vendor's own pages on 25 September 2026.

The last four weeks rearranged the frontier-model market. OpenAI released GPT-6 Astra on 3 September and then GPT-6 Sol and Luna on 22 September. Anthropic shipped Claude Fable 5.1 on 1 September and Claude Opus 5.5 on 22 September. Google launched Gemini 3.8 Flash on 2 September. Each launch cut prices, raised context windows or both, which means any model decision made before September is worth revisiting.

This guide compares the models on the four things that decide most deployments: what they cost, how much they can read, what the vendors' own benchmarks say, and which surfaces they run on. It ends with a decision table you can apply to your workload.

The contenders at a glance

Sources: OpenAI pricing and model pages; Anthropic pricing; Gemini API pricing and latest-model page; DeepSeek pricing; xAI release notes. A dash means the vendor page does not state the figure. Anthropic's pricing page states that all Claude models from 4.6 onwards include the full 1M-token context at standard pricing.

Three pricing rules hide inside that table:

1. OpenAI's long-context surcharge. On every GPT-6 model, a request with more than 272,000 input tokens is billed at 2x the input and cache rates and 1.5x the output rate for the entire request. A 500,000-token prompt to Sol therefore costs $4 per million input, the same as Opus 5.5 at any length.
2. Google's promotional clock. Gemini 3.8 Flash's $0.75 / $3.75 rate ends on 31 December 2026 and becomes $1.50 / $7.50. Budget 2027 on the higher figure.
3. DeepSeek's off-peak discount. Outside 01:00–04:00 and 06:00–10:00 UTC on weekdays, DeepSeek charges half its peak rate: $0.15 / $0.60 per million tokens.

What each model is for

GPT-6 Astra: the frontier, at a frontier price

Astra is OpenAI's most capable model and the only one in the family that does not offer a none reasoning effort: it always thinks, from low to max. OpenAI's model page lists a 1,050,000-token context, 922,000 maximum input tokens and 128,000 maximum output tokens, with Chat Completions, Responses and Batch endpoints supported and computer use, hosted shell, MCP and tool search among the built-in tools. At $10 / $50 it costs five times Sol. Use it when a single decision is expensive: legal or financial analysis, security review, planning a multi-day agent run where a bad plan wastes hours of cheaper model time.

GPT-6 Sol: the new default

Sol shares Astra's context and output limits and adds a none reasoning mode for latency-sensitive calls. OpenAI told TechCrunch that Sol makes about half as many mistakes as its predecessor on the company's internal factuality evaluation, and the pricing page confirms it is half the price of GPT-5.6 Sol ($4 / $20). VentureBeat's launch coverage reported OpenAI's benchmark figures of 68.8% on DeepSWE 1.1 and 60.5% on OSWorld 2.0 at xhigh effort; those are vendor numbers, not independent measurements. One API detail to note: on Chat Completions, function calling only works with reasoning_effort set to none, so agentic workloads should use the Responses API.

GPT-6 Luna: the volume play

Luna is priced at one twentieth of Sol and one hundredth of Astra, with the same million-token window and, per its model page, the most recent knowledge cutoff of the three (18 May 2026). VentureBeat reported 66.6% on DeepSWE 1.1, only two points behind Sol. It is also the model OpenAI gives to Free and Go users in the desktop app. For classification, extraction, routing, summarisation and first-pass drafts at scale, Luna is the model to benchmark first; if it passes your evaluation, nothing else is close on cost.

Claude Fable 5.1: Anthropic's top model

Fable 5.1, released 1 September, is priced like Astra at $10 / $50 but with a much cheaper cache: reads cost $0.25 per million, 2.5% of the input price, versus 10% on Astra. For agents that re-read a large fixed context on every turn, that gap matters more than the headline price. Batch requests are half price. Anthropic's pricing page also notes that its models from Claude 4.7 onwards use a newer tokenizer that produces roughly 30% more tokens for the same text, so compare cost per task, not per token, when you benchmark against OpenAI.

Claude Opus 5.5: the coding and agent workhorse

Opus 5.5 is the model Anthropic is putting behind Claude Code by default. The announcement publishes 66.4% on Terminal-Bench 4.0, 54.4% on FrontierCode v1.1, 57.8% on CursorBench 4.0, 81.8% on OSWorld 2.0, 67.7% on Humanity's Last Exam with tools and 1846 Elo on GDPval-AA v2.1, and says the model runs 40% cheaper than Opus 5 with output more than 30% faster. Anthropic also reports better resistance to prompt injection and lower token consumption per task. At $4 / $20 it is twice Sol's list price, but Anthropic's 1M context has no surcharge, and a "fast mode" at $8 / $40 is available when latency matters more than cost. Sonnet 5.5 and Haiku 5.5 are due "in the coming weeks."

Gemini 3.8 Flash: the cheapest frontier-adjacent model

Google's announcement positions 3.8 Flash as outperforming larger models on DeepSWE v1.1 at lower cost, and cites 54.9% on HLE-Verified and 47.2% pass@1 on CWE-Bench patching. The model runs in the Gemini API, AI Studio, Antigravity, the Gemini app for Pro and Ultra subscribers, and AI Mode in Google Search. It has a 1M-token input window but only 64K output tokens, and three thinking levels (low, medium, high). A separate 3.8 Flash Cyber variant for vulnerability detection and patching is restricted to Google's Fairwind programme for governments, critical-infrastructure operators and software maintainers.

DeepSeek V4.1-Flash and Grok 4.7: the alternatives

DeepSeek V4.1-Flash (10 September) is a 552-billion-parameter mixture-of-experts model that activates 8B parameters for input and 16B for output, adds native vision, and is served under the deepseek-flash name with a 1M context and a 384K output limit. DeepSeek says third-party tests put it ahead of the larger V4-Pro, which is being phased out. The weights are on Hugging Face, so it is also the best candidate here for self-hosting; see our guide to running open-weight models locally.

Grok 4.7 from xAI lists a 500K context window at $2 / $6 for prompts under 200K tokens and $4 / $12 above. It is the cheapest output price in the workhorse tier, at the cost of a smaller window and a less mature tooling ecosystem.

Decision table

How to run a fair comparison

Vendor benchmarks are useful for ranking, not for predicting your results. A ten-step evaluation that takes an afternoon:

1. Collect 50 to 100 real inputs from production, with the output you consider correct.
2. Fix the prompt, temperature and tool definitions across models. Do not tune per model on the first pass.
3. Run each model at its default reasoning effort, then once more at the cheapest effort that still passes.
4. Record cost per request from the API usage fields, not from list prices; tokenizers differ by up to 30%.
5. Score correctness with a rubric or a stronger model as judge, and spot-check the judge.
6. Measure latency at the 95th percentile, not the mean.
7. Compare cost per correct answer. A model that is 30% cheaper per token but 20% less accurate is more expensive.
8. Re-run with prompt caching enabled and the static context first; cache-read pricing changes the ranking for agents.
9. Check the long-context surcharge if any inputs exceed 272K tokens on OpenAI or 200K on xAI.
10. Repeat quarterly. September 2026 showed that a quarter is long enough for every price in this article to change.

The LLM token calculator gives you input sizes for step 4, and the LLM API pricing comparison tracks the list prices with verification dates. For a broader view of how the assistants compare from a user's chair rather than a developer's, see ChatGPT vs Claude vs Gemini.

FAQ

Is GPT-6 Sol better than Claude Opus 5.5? Neither vendor publishes results on the other's benchmark set, so there is no clean answer. OpenAI's reported DeepSWE and OSWorld numbers and Anthropic's Terminal-Bench, CursorBench and OSWorld numbers are not directly comparable. On price, Sol is half of Opus 5.5 at list; on long context, Opus has no surcharge. Run both on your own tasks.

Which is the cheapest frontier-family model? GPT-6 Luna at $0.10 per million input and $0.50 per million output tokens. Among open-weight hosted models, DeepSeek V4.1-Flash is $0.15 / $0.60 off-peak.

Do I need Astra or Fable 5.1? Only if a wrong answer is expensive enough to justify five times the cost of Sol or Opus 5.5. A common pattern is to use Sol or Opus for execution and call Astra or Fable once per task for planning or final review.

Will these prices last? Gemini 3.8 Flash's price is explicitly promotional until 31 December 2026. OpenAI and Anthropic have each changed prices twice this quarter. Check the vendor page before committing a budget.