The Week Every AI Model Decided To Drop

Table of Content

• The new model pile-up
• Grok 4.5: Elon’s agentic workhorse
• GPT-5.6 Sol, Terra, Luna: three heads, same dragon
• Anthropic Fable 5: Mythos-lite with a luxury tax
• Benchmarks: leaderboard porn for LLMs
• Price cards vs real cost per task
• Tokens per task: thinking like a bill, not a prompt
• So what do you actually pick?

The new model pile-up

Grok 4.5 just landed, GPT-5.6 rolled out in three flavors (Sol, Terra, Luna), and Anthropic decided to bring back its Mythos-class tech as Fable 5. Vendors are screaming about “agents”, “frontier intelligence” and “city-scale training compute”, while you’re just wondering how many euros a 3 k-token request is going to burn.

Short version: the models are great, the benchmarks are shiny, and the price cards are where projects quietly die.

Grok 4.5: Elon’s agentic workhorse

Grok 4.5 is xAI/SpaceXAI’s new flagship, built on a 1.5-trillion-parameter V9 foundation model, fine-tuned heavily on Cursor coding data. It’s pitched as “Opus-class” but “faster, more token-efficient and lower cost” than Anthropic’s top model, with a strong focus on coding, finance and agentic workflows that run mostly on autopilot.

Key bits for devs:

• Context window: 500 k tokens, enough to swallow most monorepos or legal doc sets without sharding your prompt every 5 minutes.
• Pricing: 2 USD per 1M input tokens, 6 USD per 1M output tokens via the Grok 4.5 API and Grok Build in Cursor.
• Positioning: explicitly marketed as cheaper than Claude Opus 4.8 (5 USD input / 25 USD output) while still “Opus-class”.

So yes, it’s a beast, but importantly it’s a discount beast compared to some of the others.

For more details, see the official xAI developer documentation.

GPT-5.6 Sol, Terra, Luna: three heads, same dragon

OpenAI’s GPT-5.6 isn’t one model, it’s a small family: Sol (flagship), Terra (balanced), and Luna (fast/cheap). All three are now live in the API with official price cards and specs instead of vague keynote slides.

Pricing per 1M tokens (input / output):

• Sol: 5 USD / 30 USD — the big brain, same sticker price as GPT-5.5.
• Terra: 2.5 USD / 15 USD — framed as “GPT-5.5-class at half the cost”.
• Luna: 1 USD / 6 USD — the cheapest tier, sitting between GPT-5 and GPT-5 Mini in price.

Specs in one line:

• Sol: up to 1.1M context, 128 k output; “Ultra” mode hits 91.9 % on Terminal-Bench 2.1 for hardcore coding/terminal tasks.
• Terra: 1M context, 128 k output, around 84.3 % on Terminal-Bench, roughly GPT-5.5-level intelligence.
• Luna: 400 k context, 64 k output, still around 82.5 % on Terminal-Bench and designed for high-volume, lower-stakes work.

So Sol is “maximum brain, maximum bill”, Terra is the sweet spot, and Luna is the thing you point your CRUD agents at when you still like your CFO.

Anthropic Fable 5: Mythos-lite with a luxury tax

Anthropic’s Fable 5 is basically a toned-down version of its private Mythos model: same architecture, but with hard guardrails that kick you back to Opus 4.8 when you wander into cybersecurity, biology or other spicy domains. It’s positioned as their most capable widely available Claude model, tuned for long-running agents doing software engineering, analytics, finance and complex enterprise workflows.

Highlights:

• Context window: up to 1M tokens, with outputs up to 128 k, matching the “giant context” trend.
• Benchmarks: third-party tests report Fable hitting 90 % on some complex analytics benchmarks and outperforming other models on long-running data workflows and app generation.
• Pricing: 10 USD per 1M input tokens and 50 USD per 1M output tokens — double Opus 4.8 and the most expensive widely available Claude tier.

Oh, and Anthropic requires 30-day data retention on all Fable/Mythos traffic “for safety”, which is great if you’re them, less great if you’re doing sensitive work.

Benchmarks: leaderboard porn for LLMs

On paper, GPT-5.6 Sol in Ultra mode now sits at the top of Terminal-Bench 2.1 with 91.9 %, beating GPT-5.5 (83.4 %), Claude Mythos 5 (88 %) and Claude Opus 4.8 (78.9 %). Terra and Luna follow just behind at 84.3 % and 82.5 %, respectively, which is still very much “production-grade” territory.

Anthropic counters with Fable 5 winning some long-horizon analytics and agentic benchmarks: Hex reports it as the first model to hit 90 % on their complex analytics suite, and other partners praise its ability to one-shot full apps and beat every other model in their internal evals. SpaceXAI claims Grok 4.5 matches or exceeds Claude Opus on internal engineering benchmarks while running faster and cheaper, especially on coding and knowledge work.

TL;DR: everyone has a slide proving they’re the best at something. Your job is to decide which something actually maps to your production workloads.

Price cards vs real cost per task

Token prices are cute, but what matters is what you pay for the actual requests your app makes all day.

Take a simple, very real workload: 3 000 tokens of context in, 500 tokens out — roughly “give the model a page of code + some docs and ask for a fix”. For this pattern, the cheapest model is Llama 3.1 8B Instant at 0.19 USD per 1 000 tasks, while the most expensive is 100 USD per 1 000 tasks — a 526x spread for the same task shape.

Now plug our new toys into that 3 k-in / 500-out shape, using their list prices:

• Grok 4.5 (2 USD in / 6 USD out): ≈0.009 USD per task, ~9 USD per 1 000 tasks.
• GPT-5.6 Luna (1 USD / 6 USD): ≈0.006 USD per task, ~6 USD per 1 000 tasks.
• GPT-5.6 Terra (2.5 USD / 15 USD): ≈0.015 USD per task, ~15 USD per 1 000 tasks.
• GPT-5.6 Sol (5 USD / 30 USD): ≈0.03 USD per task, ~30 USD per 1 000 tasks.
• Claude Fable 5 (10 USD / 50 USD): ≈0.055 USD per task, ~55 USD per 1 000 tasks.

Same request shape, 3 k + 500 tokens. Pick Fable instead of Luna and your agent budget goes up by roughly 9x for that workload, even before you start spawning sub-agents and retries.

This is why “Sol vs Terra vs Luna” or “Grok vs Fable” is not just a benchmark argument, it’s a finance problem.

For comprehensive pricing comparisons, see Benchr’s LLM pricing tracker.

Tokens per task: thinking like a bill, not a prompt

Most dev teams underestimate how many tokens their agents chew through when you start doing “serious” work:

• Long-context agents: 50 k–200 k tokens in a single turn to process docs, repos or multi-file diffs is now normal with Grok 4.5, Terra/Sol, or Fable.
• Long-running agents: Anthropic and OpenAI both brag about agents that can run for hours or days, breaking tasks into many subtasks — each with its own prompt and response on the bill.
• Tool-calling + retries: every “call function, oops, that failed, retry, re-plan, summarize logs” loop is several extra thousand tokens, multiplied by your concurrency.

Once you start thinking in “tasks per month”, the delta between Luna/Terra/Grok and Sol/Fable stops being cosmetic and starts being “do we hire another engineer or feed this model”.

So what do you actually pick?

If you’re shipping a SaaS and care about both performance and cost:

• Use Luna or Grok 4.5 for high-volume, routine agents (ticket triage, CRUD APIs, simple coding helpers). They’re fast, strong, and inexpensive per 1 000 tasks.
• Use Terra when you need GPT-5.5-level brains but don’t want Sol’s flagship pricing — it’s literally framed as 5.5-class at half cost.
• Reserve Sol Ultra and Fable 5 for the “hard mode” workloads: complex analytics, safety-critical systems, or places where a few saved human hours are worth a chunky token bill.

And absolutely do what the pricing studies recommend: take your actual task shapes (average context, average output), multiply by each model’s price card, and rank them by cost per 1 000 tasks before you fall for the shiniest benchmark.

The hype is fun, the launch blogs are glossy, but at the end of the month the only benchmark that really matters is “tokens per task × tasks per month × price per token”.