Which is the best model in September 2026: Grok 4.7, Claude Fable 5.1, GPT-6 Astra or GPT-5.6 Sol?

For raw capability, Claude Fable 5.1 and GPT-6 Astra tie at 53 on the Artificial Analysis Intelligence Index v4.3.2, with GPT-5.6 Sol below them and Grok 4.7 at 46 (rank 16 of 655). For agentic coding, GPT-6 Astra leads Terminal-Bench 4.0 at 60% on Artificial Analysis's run, Fable 5.1 scores 55% and Grok 4.7 26%. For price-performance, Grok 4.7 at $2/$6 per million tokens is the pick: it beats GPT-5.6 Sol on four of seven rows in xAI's table at half the price and leads every model on EEBench and the Harvey Legal Agent Benchmark. Best overall quality: Fable 5.1. Best cost per completed task at the frontier: GPT-6 Astra. Best budget frontier: Grok 4.7.

How do Grok 4.7, GPT-5.6 Sol, Claude Fable 5.1 and GPT-6 Astra compare on price?

List prices per million tokens as of September 22, 2026: Grok 4.7 $2 input / $6 output ($0.50 cached; doubles above 200K prompt tokens); GPT-5.6 Sol $4 / $20; Claude Fable 5.1 $10 / $50 with $0.25 cache reads; GPT-6 Astra $10 / $50 with $1 cached input, ~$12.50 cache writes and a ~2x long-context premium above 272K tokens. Grok 4.7 is 5x cheaper than the two flagships per token, but Artificial Analysis found it emits ~81,000 output tokens per hard task, so its per-task advantage is smaller than the sticker gap.

Is Grok 4.7 good enough to replace Claude Fable 5.1 for coding?

Not for long-horizon agentic coding. On Terminal-Bench 4.0 Artificial Analysis measured Grok 4.7 at 26% against 55% for Claude Fable 5.1; xAI's own table has 38.0% versus 57.9%. Fable 5.1 also leads CursorBench 4.0 (51.8% vs 46.3%) and GDPval (1,735 vs 1,695 Elo). Grok 4.7 is competitive on DeepSWE v1.1 (71.0% vs 70.0%) and AA Briefcase (1,657 vs 1,678), so for shorter, well-scoped coding tasks it delivers most of the quality at one-fifth the token price. Use Grok 4.7 for bulk and Fable 5.1 for the hardest 10-20% of tasks.

When should I use GPT-5.6 Sol instead of Grok 4.7?

Use GPT-5.6 Sol when you need OpenAI's ecosystem (Codex, ChatGPT Work, Responses API tooling), when DeepSWE-style software engineering matters (72.7% vs 71.0%), for clinical reasoning (HealthBench Professional 60.5% vs 56.7%), or when you are migrating off GPT-5.5 before its October 14, 2026 retirement from ChatGPT and Codex. Use Grok 4.7 when price dominates: it costs half as much per token and beats Sol on CursorBench 4.0, EEBench, AA Briefcase and the Harvey Legal benchmark in xAI's table.

Quick Answer

Grok 4.7 vs Claude Fable 5.1 vs GPT-6 Astra vs GPT-5.6 Sol

Published: September 22, 2026

The short answer

Four frontier-tier models, three price points, no single winner. As of September 22, 2026:

Claude Fable 5.1 ($10/$50) — highest quality on most benchmarks, cheapest cache reads.
GPT-6 Astra ($10/$50) — ties Fable on the Artificial Analysis index, leads Terminal-Bench 4.0, emits fewer tokens so costs less per task.
GPT-5.6 Sol ($4/$20) — OpenAI’s mid-tier and the named replacement for GPT-5.5; strong on DeepSWE and clinical reasoning.
Grok 4.7 ($2/$6, launched September 21, 2026) — the budget frontier: beats Sol on four of seven rows at half the price, but well behind the flagships on long agentic work.

Price per million tokens

	Grok 4.7	GPT-5.6 Sol	Claude Fable 5.1	GPT-6 Astra
Input	$2.00	$4.00	$10.00	$10.00
Cached input	$0.50	—	$0.25	$1.00 (writes ~$12.50)
Output	$6.00	$20.00	$50.00	$50.00
Long-context premium	2x at ≥200K prompt tokens	—	None published	~2x input above 272K
Context window	500K	—	1,000K	1,050K
Released	Sep 21, 2026	Jul 2026	Sep 3, 2026	Sep 3, 2026

Benchmarks

xAI’s launch table (xAI-run; Grok 4.7 xHigh, GPT-5.6 Sol Max, Fable 5.1 Max)

Benchmark	Grok 4.7	GPT-5.6 Sol	Fable 5.1
CursorBench 4.0	46.3%	41.7%	51.8%
DeepSWE v1.1	71.0%	72.7%	70.0%
EEBench	64.0%	39.4%	56.4%
AA Briefcase v1.1	1,657	1,487	1,678
Terminal-Bench 4.0	38.0%	37.3%	57.9%
Harvey Legal Agent Benchmark	19.6%	2.5%	6.7%
HealthBench Professional	56.7%	60.5%	62.1%
GDPval (Elo)	1,695	—	1,735 (GPT-6 Astra: 1,542)

Independent (Artificial Analysis, September 21, 2026)

Metric	Grok 4.7	Fable 5.1	GPT-6 Astra
Intelligence Index v4.3.2	46 (rank 16/655)	53	53
Terminal-Bench 4.0	26%	55%	60%
Output tokens per index task	~81,000	—	Fewer than Fable (Astra ≈ 1/3 cost per coding task)
Hallucination rate (AA-Omniscience)	29%	—	—

Note the version split: on the earlier index used at the September 3 launches, Fable 5.1 scored 65.6 and GPT-6 Astra 61.1. v4.3.2 rebased the scale, so compare within a version only.

Where each model wins

Claude Fable 5.1 — best quality, best caching. Leads CursorBench, Terminal-Bench (xAI table), AA Briefcase, HealthBench and GDPval. Its $0.25 cache reads make it the cheapest of the four for agents that re-read a large stable context. GA on Anthropic API, Bedrock, Vertex and Foundry. Full head-to-head: GPT-6 Astra vs Claude Fable 5.1.

GPT-6 Astra — best long-horizon agent per dollar at the top tier. 60% on Terminal-Bench 4.0 in Artificial Analysis’s harness, the highest of the four, and roughly one-third of Fable’s cost per completed coding task because it emits fewer tokens. Weak spot: 1,542 on xAI’s GDPval chart, the lowest of the models shown, and expensive cache writes.

GPT-5.6 Sol — the safe OpenAI default. Best DeepSWE v1.1 score (72.7%), second on HealthBench, and the model OpenAI tells Codex users to move to when GPT-5.5 retires from ChatGPT and Codex on October 14, 2026. Loses to Grok 4.7 on four of seven rows at twice the price.

Grok 4.7 — the price-performance play. Only model under $10 output that is within a point of the flagships on DeepSWE and AA Briefcase, and it leads every model on EEBench (64.0%) and the Harvey Legal Agent Benchmark (19.6%, nearly 3x Fable’s 6.7%). Falls to 26-38% on Terminal-Bench 4.0 and 46 on the AA index. Details: What is Grok 4.7?

Cost per task, not per token

Sticker price misleads in both directions. Grok 4.7 spends ~81,000 output tokens on a hard Artificial Analysis task; at $6/MTok that is ~$0.49 per task. A flagship at $50/MTok that finishes the same task in 25,000 tokens costs ~$1.25. Grok’s real advantage is therefore ~2.5x, not the 8x the output prices imply — still large, but budget on measured tokens, not rate cards. GPT-6 Astra’s token efficiency is why it undercuts Fable 5.1 on cost per task despite identical pricing.

Decision guide

You need	Pick
Highest quality, cache-heavy agents	Claude Fable 5.1
Multi-hour terminal agents, lowest frontier cost per task	GPT-6 Astra
OpenAI stack, GPT-5.5 replacement, clinical	GPT-5.6 Sol
Bulk coding at the lowest price; legal, EE workflows	Grok 4.7
One router	Grok 4.7 default → Fable 5.1 or Astra for the hardest 10-20%