Comparison Report: GPT-5.6 Sol, Fable5, and the Muse Spark 1.1, Grok 4.5,
Written By AI Researchers at Distributedapps.ai
Five frontier models shipped or updated inside one month. Meta's Muse Spark 1.1 landed July 9. Grok 4.5 landed July 8. OpenAI pushed GPT-5.6 with its Sol flagship on July 9. Anthropic's Claude Fable 5 and Claude Mythos 5 have been out since last week and still hold the top of most quality charts. If you build agents or ship code with these things, the question is no longer "which model is smartest." The question is which corner of a three-way tradeoff your workload lives in, and that question has a different answer for almost every team I talk to.
Here is the split for this post. The full comparison is free: specs, pricing, the benchmark head-to-head, and my read on what separates Fable from Mythos. The paid section below the wall holds the part you can act on: the routing playbook I'd run in production, five insights the charts hide, an FAQ from questions readers sent me, and my predictions for the rest of 2026.
The five contenders in one picture
Read the price column twice, because it carries the story. Muse Spark 1.1 costs $1.25 per million input tokens and $4.25 out. Claude Fable 5 costs $10 in and $50 out. That is an 8x gap on input against a model Meta claims beats Opus 4.8 on agentic work. Grok 4.5 sits at $2 and $6 with the best tokens-per-second of the group at around 80. GPT-5.6 splits the difference with tiers: Sol is the $5/$30 flagship, Terra sits in the middle, and Luna is the cheap fast option.
Context windows have converged. Four of the five sit at or near one million tokens. Grok 4.5 is the outlier at 500K, which sounds like a weakness until you check how often a real agent loop needs more than that in one shot. It almost never does.
Availability is the quiet differentiator. Fable 5 is everywhere: API, Bedrock, the usual suspects. Muse is still an API preview on Meta's new Model API. Mythos 5 you cannot buy at all. It ships through trusted access only, under Project Glasswing for cyber defenders plus a small biology research program.
What the benchmarks say, and where they disagree
The headline number is Fable 5's roughly 80% on SWE-Bench Pro. Grok 4.5 and GPT-5.6 Sol sit fifteen points back in a near tie at 64.7% and 64.6%, with Muse at 61.5%. A fifteen point gap on the hardest public software benchmark is not noise. It is the largest lead any model has held on a coding bench since the original SWE-Bench saturated.
But flip to DeepSWE 1.1 and the order inverts: Sol takes it at 72.7% with Fable at 69.7% and Grok back at 53%. Terminal-Bench 2.1 goes to Sol again at 88.8%. So who is the best coding model? Wrong question. The two benches reward different things. SWE-Bench Pro rewards long-horizon patch quality on real repos, and Fable's edge there matches what I see in practice on gnarly multi-file changes. DeepSWE and Terminal-Bench reward tight tool loops and shell fluency, which is where OpenAI has tuned its stack since Codex became the center of its developer story.
Muse's row looks thin because Meta has published few independent numbers so far. The two it promotes are real signals though: 88.1 on MCP Atlas is state of the art for tool orchestration, and 54.7 on JobBench beats both Opus 4.8 and GPT-5.5. Meta built this model for agents that click, watch, and route between tools, not for grinding out repo patches, and the numbers land where the design points.
Grok 4.5 never wins a column. It also never falls far behind, and it charges a fifth of Fable's price while running faster than everything else. On the Artificial Analysis Intelligence Index the top three pack inside six points: Fable at 59.9, Sol at 58.9, Grok at 54. The intelligence gap between "best" and "best value" has never been this narrow at the frontier.
The triangle
A year ago the frontier was a ladder and you picked the top rung you could afford. July 2026 is a triangle. One corner is peak quality: Fable 5 and Mythos 5, priced like the premium products they are. One corner is platform depth: GPT-5.6 Sol, with Codex, multi-agent tooling, computer use, and the deepest integration surface in the industry. One corner is efficiency: Grok 4.5 and Muse Spark 1.1, where frontier-adjacent quality costs pocket change and runs fast.
The triangle matters because no vendor is even trying to win all three corners anymore. Anthropic did not chase Muse's price. Meta did not chase Fable's SWE-Bench score. xAI shipped speed and cost and let the peak-quality crown go. Each player picked a corner, which means you get to pick one too, and picking wrong now costs real money in one direction or real quality in the other.
Fable vs Mythos: one model, one gate
Fable 5 and Mythos 5 share the same base model. The difference is a safety layer. Fable runs classifiers over your session, and when a query trips the cyber, biology, chemistry, or distillation detectors, the response silently falls back to Opus 4.8. Anthropic says this touches under 5% of sessions. Mythos removes that gate for approved organizations: cyber defense teams through Project Glasswing, plus a limited biology research cohort. Same price, same context window, same weights.
For most builders the gate is invisible. For security teams it is the whole decision. A pentest shop or a detection engineering team will trip the classifier on exactly the queries they care most about, and each fallback hands them a weaker model at the worst moment. If that describes you and you cannot get Mythos access, GPT-5.6 Sol is the strongest cyber model you can buy without an approval process, and that fact alone will move security spend toward OpenAI this quarter.
That is the free read: Fable holds the quality crown, Sol owns the platform and the open cyber lane, Grok and Muse make the frontier cheap, and Mythos is the specialist tool most of us will never touch. Below the wall I get practical: the exact routing setup I would run, what the charts hide, your questions, and where I think this goes by December.
For paid subscribers, the practical half continues below.
Everything above is free. The paid section adds four things: (1) the cost-aware routing playbook I would run in production, with the exact escalation loop and code; (2) five insights the benchmark charts hide, from token-efficiency math to the shape of the Fable safety gate; (3) an FAQ on model choice, Mythos access, and computer use; and (4) five predictions for the rest of 2026.
Unlock it, and every paid deep-dive on this publication, here: https://kenhuangus.substack.com/subscribe?coupon=302342d9.
