GPT-5.6 Sol vs Fable 5 vs Grok 4.5: Long Horizon Agents, Reasoning, and Cost Tested (July 2026)

OpenAI made all three GPT-5.6 tiers, Sol, Terra, and Luna, generally available on July 9, and the flagship Sol tier arrived with claims that reset the frontier conversation. It posts the top public score on Terminal-Bench 2.1, a new high on Agents Last Exam, and general intelligence one point behind Claude Fable 5 while costing roughly a third as much per token. If you are choosing a model to run long, expensive, multi step agent workflows, those three facts change the shortlist. Here is how Sol stacks against Fable 5 and Grok 4.5 on the numbers that decide the build, and where the launch benchmarks flatter Sol more than your workload will.

Metric	GPT-5.6 Sol	Fable 5	Grok 4.5
Agents Last Exam	53.6	~40.5	not reported
Terminal-Bench 2.1	91.9% (Ultra)	strong	83.3%
Artificial Analysis Intelligence Index (max)	59	60	mid pack
Input price, per 1M tokens	$5.00	premium tier	$2.00
Output price, per 1M tokens	$30.00	premium tier	$6.00

Two of those rows deserve a second read. Agents Last Exam evaluates long running professional workflows across dozens of fields, and Sol setting a new high of 53.6, more than thirteen points clear of Fable 5, is the single most consequential number in the launch, because long horizon task completion is where most agent products actually fail. And the intelligence index row, Sol at 59 against Fable 5 at 60, tells you the raw capability gap at the very top is now one point, which reframes the decision away from who is smartest toward who is cheapest and most reliable over long tasks.

Agents Last Exam Is the Number That Matters

Most benchmark leads do not survive contact with a real deployment, because they measure single turn skill on a clean prompt. Agents Last Exam is different. It scores a model running professional workflows that span many steps, tools, and decisions, the kind of task where an agent has to stay coherent for minutes and recover from its own mistakes rather than answer a question. That is precisely the regime where products break, so a thirteen point lead there for GPT-5.6 Sol is worth more than a larger lead on a static reasoning test.

Grok 4.5 did not post a comparable Agents Last Exam figure at launch, but its Terminal-Bench 2.1 result of 83.3 percent, well behind Sol's 91.9 percent in Ultra mode, suggests it trails on the longest and most tool heavy tasks even though it is excellent on tight terminal loops. Fable 5, at roughly 40.5 on Agents Last Exam, remains a formidable reasoner but no longer owns the long horizon crown. We measured Fable 5's reasoning strengths in detail against Sonnet 5 on GPQA Diamond and Humanity's Last Exam, and the story there is that Fable 5 is a research and hard reasoning specialist more than an agent runner.

The One Point Intelligence Gap Changes the Math

For two years the top of the intelligence index was the whole argument. When the best model was clearly the smartest, you paid its premium and moved on. GPT-5.6 Sol scoring 59 against Fable 5's 60 collapses that argument. When the capability difference at the frontier is one point and the cost difference is roughly three to one, the rational default flips from best model to best value, and you reserve the premium model for the specific tasks where that one point demonstrably pays for itself.

This is the same dynamic we flagged when Fable 5 met GPT-5.5 and Grok 4.3 on hard math: the leader wins the headline, but the margin is thin enough that cost, latency, and reliability decide the deployment. What is new with Sol is that the cheaper model is also the one winning the agent benchmark, so the usual tradeoff, pay more for quality or less for value, partly dissolves. On long horizon agent work Sol appears to be both the more capable and the cheaper option relative to Fable 5, which is an unusual place for an OpenAI flagship to land.

A Simple Way to Price the Three for Your Workload

Sticker price per token is not your bill. What you pay is the price per token multiplied by how many tokens a model burns to finish your task, and a model that completes a long workflow in fewer, cleaner steps can be cheaper end to end even at a higher per token rate. Here is a small model you can adapt.

def blended_task_cost(in_price, out_price, in_tok, out_tok, success_rate):
    # cost of one successful long-horizon task, amortizing failed runs
    per_run = (in_tok * in_price + out_tok * out_price) / 1_000_000
    return round(per_run / success_rate, 4)

# illustrative token counts for a long multi-step agent task
sol   = blended_task_cost(5.00, 30.00, 120_000, 40_000, 0.72)
grok  = blended_task_cost(2.00,  6.00, 120_000, 40_000, 0.58)
print("GPT-5.6 Sol:", sol, "Grok 4.5:", grok)

Plug in your own token counts and, crucially, your own success rate on your own tasks rather than the illustrative figures above. The point of the function is that success rate on long tasks and price per token pull in opposite directions: Grok 4.5 is far cheaper per run, but if it fails long workflows more often, its cost per completed task can converge with or exceed Sol's. This is the same accounting we applied to tool use agents in the tau-bench comparison, and the lesson repeats: measure cost per completed task, not price per token.

Who to Pick

Pick GPT-5.6 Sol for long horizon agent products where task completion rate is the metric that makes or breaks the experience. Its Agents Last Exam and Terminal-Bench leads, combined with pricing far below Fable 5, make it the strongest default for autonomous multi step workflows in mid 2026. The Terra and Luna tiers, at lower price points, are worth testing for the high volume steps of those same workflows where you do not need the flagship.

Pick Fable 5 when the task is hard reasoning or research grade analysis rather than long agentic execution, where its top of index capability still earns its premium, and where a single deep answer matters more than a fifty step workflow. Pick Grok 4.5 for cost sensitive, high volume, terminal driven work where its low price and fast serving dominate and the tasks are short enough that its long horizon gap does not bite. As with the coding matchups, the best architecture usually routes across all three rather than standardizing on one.

When This Applies to Your Stack

If you are shipping an agent that runs long, do not choose from a launch benchmark. Build a small evaluation set from your own real workflows, run Sol, Fable 5, and Grok 4.5 against it, and score completion rate and cost per completed task, not per token. The frontier is now close enough at the top that the right choice is almost always a workload specific answer, and often a router that sends most steps to a cheaper tier and escalates only the hard ones.

If your team wants that evaluation harness, model routing, and cost model built against your own agent workflows rather than a vendor's press release, Contra Collective designs and implements AI integration and agent engineering that stays reliable and affordable as it scales. New flagships will keep landing; the discipline of testing them on your own long tasks is what turns a launch into a decision.

FAQ

Is GPT-5.6 Sol better than Fable 5? On long horizon agent tasks, yes by a clear margin: Sol posts 53.6 on Agents Last Exam against roughly 40.5 for Fable 5, and leads Terminal-Bench 2.1. On raw general intelligence they are within one point, and Fable 5 remains the stronger pick for hard reasoning and research grade analysis. It depends on whether you need an agent runner or a deep reasoner.

How much cheaper is GPT-5.6 Sol than Fable 5? Sol lists at $5 input and $30 output per million tokens and sits at roughly a third of Fable 5's cost per the Artificial Analysis index. Fable 5 remains a premium tier model, so for long agent workflows Sol is both the higher scoring and the cheaper option, an unusual combination.

Where does Grok 4.5 fit against these two? Grok 4.5 is the cost and speed play at $2 input and $6 output per million tokens. It wins short terminal driven tasks and high volume work, but it trails Sol on the longest agentic workflows and did not post a comparable Agents Last Exam score. Use it where tasks are short and volume is high.

Should I switch my agent to GPT-5.6 Sol now? Test before you switch. Build an evaluation set from your own workflows and measure completion rate and cost per completed task. Sol's benchmarks are strong enough to justify the test, but public numbers and your workload can diverge, and a routing setup that uses cheaper tiers for easy steps often beats a single model swap.

What are the Terra and Luna tiers for? They are the lower priced members of the GPT-5.6 family, aimed at the high volume steps of a workflow where the flagship is overkill. A common pattern is to run most of an agent on Terra or Luna and escalate only the hard steps to Sol, which lowers blended cost without giving up completion rate on the parts that matter.