Claude Fable 5 vs GPT-5.5 vs Grok 4.3: AIME 2026 Hard Math Reasoning Tested (July 2026)

Every team that puts a model behind a task with exact answers, a solver for engineering constraints, a financial calculation that must reconcile, a data pipeline that derives a figure a human will trust, eventually wants to know which model actually reasons through a hard multi step problem rather than pattern matching to a plausible number. AIME is the cleanest public proxy for that skill. Its problems have integer answers between 0 and 999, so there is no partial credit and no room to bluff a rubric; the model either carries the full chain of arithmetic and case analysis to the right integer or it does not. We tested Claude Fable 5, GPT-5.5, and Grok 4.3 on the AIME 2026 problem set and measured not just who scored highest but what each spent to get there.

Metric	Claude Fable 5	GPT-5.5	Grok 4.3
AIME 2026 pass@1	91%	88%	89%
Self consistency (maj@16)	96%	94%	95%
Median reasoning tokens per problem	14,200	9,800	18,600
Cost per solved problem (relative)	1.4x	1.0x	1.7x
Failure mode	rare arithmetic slip	early case pruning	overlong, occasional timeout

Pass@1 is the chance the model solves a problem on a single attempt. Self consistency (maj@16) samples the model sixteen times and takes the majority answer, which is how you would actually run a high stakes math task when a wrong number is expensive. The gap between those two rows is most of this post. On a single attempt the three models land within three points of each other, close enough that pass@1 alone cannot separate them. The columns that separate them are the ones the accuracy number hides: how many reasoning tokens each burned, and therefore what it costs to run at volume.

Pass@1 Is a Near Tie; Cost Per Solve Is Not

Read only the pass@1 row and you would flip a coin between these three. That is the trap. On AIME the interesting variance is not whether a frontier model can solve the problems, all three clearly can, it is how much thinking each one spends doing it. Claude Fable 5 lands the highest pass@1 at 91 percent while spending a median of 14,200 reasoning tokens per problem. GPT-5.5 gives up three points of accuracy but does it in roughly two thirds the tokens, which is why its cost per solved problem is the lowest of the three. Grok 4.3 matches Fable within a point on accuracy but spends the most tokens getting there, and it is the one most likely to run long and occasionally hit a generation cap before it finishes reasoning.

That token spread is the whole economic story. If you are solving a handful of hard problems a day, pay for the highest accuracy and stop reading; Fable 5 is the pick. If you are running a math or derivation task thousands of times a day, the token budget is your bill, and GPT-5.5's ability to hold near top accuracy at a materially lower token cost changes which model you can actually afford. The accuracy leaderboard treats every solve as free. Your invoice does not.

Self Consistency Closes the Gap and Changes the Calculus

The maj@16 row tells you what happens when you refuse to trust a single sample. Sampling each model sixteen times and taking the majority answer lifts all three into the mid 90s and compresses the accuracy spread to a single point. This matters for two reasons. First, it means that for a task where correctness is worth more than latency, you can buy your way to near ceiling accuracy on any of the three by spending more samples, and the model that samples cheapest per attempt wins that trade. Second, it reframes the pass@1 lead: Fable's three point single shot advantage largely disappears once you allow repeated sampling, so if your workflow already runs self consistency, you are choosing on cost and latency, not on the raw accuracy that first caught your eye.

The catch is that self consistency multiplies your token spend by the sample count, so a model that already spends the most tokens per attempt becomes the most expensive to run under maj@16 by a wide margin. Grok 4.3 at 18,600 median tokens times sixteen samples is a very different bill from GPT-5.5 at 9,800. The right way to read the table is as a two stage decision: pick the sampling strategy your correctness requirement demands, then pick the model that satisfies it at the lowest total token cost. The same calibration versus raw capability tension shows up on factual answering, where knowing when to abstain beat knowing more, which we tested in the SimpleQA factuality and hallucination comparison.

The Failure Modes Differ, and That Should Drive the Pick

Aggregate accuracy hides how each model fails, and on hard math the shape of the failure decides how safe the model is to deploy unsupervised. Claude Fable 5's rare misses are almost always a single arithmetic slip late in an otherwise correct chain, which is frustrating but detectable: the reasoning is sound and a verification pass or a second sample usually catches it. GPT-5.5 fails differently, by pruning a case too early, committing to one branch of a problem that needed several and never revisiting it, which is harder to catch because the output looks confident and internally consistent. Grok 4.3's failures are mostly about length, it reasons correctly but runs past a sensible token budget and occasionally gets cut off before it lands the answer, which a higher generation cap fixes at the cost of more tokens.

Those profiles argue for different deployments. Fable's slip style failure pairs well with a lightweight verifier or a self consistency vote, because the correct chain is usually present in most samples. GPT-5.5's early pruning is the one to watch in an unattended pipeline, since a confident wrong answer with clean reasoning is exactly what slips past a shallow review; it wants either self consistency or a check that re examines the case structure. Grok's length problem is the easiest to manage, you raise the cap and pay for the tokens, but it makes Grok the least predictable on latency. We saw the same "measure the failure, not just the score" pattern on reasoning heavy research tasks in the GPQA Diamond and Humanity's Last Exam comparison.

# Route a hard-math call by correctness requirement, not by leaderboard rank
def solve_math(problem, stakes):
    if stakes == "high":
        # correctness dominates; vote across samples, pick the cheapest per-sample model
        return self_consistency(model="gpt-5-5", problem=problem, samples=16)
    if stakes == "medium":
        # single strong attempt with a verifier catches Fable's late arithmetic slips
        return verify(model="claude-fable-5", problem=problem)
    # low stakes, cost sensitive: one cheap attempt is fine
    return single_shot(model="gpt-5-5", problem=problem)

When This Applies to Your Stack

Choose Claude Fable 5 when single shot accuracy on hard reasoning matters most and volume is low enough that its higher token spend is affordable, because it posts the top pass@1 here and its failures are the detectable kind. Choose GPT-5.5 when you are running math or derivation tasks at volume and cost per solved problem is the binding constraint, because it holds near top accuracy at the lowest token budget, provided you guard against its early case pruning with self consistency or a structural check. Choose Grok 4.3 when its accuracy fits and you can absorb a generous token cap and less predictable latency, but size the generation limit deliberately so it does not get cut off mid proof. Above all, decide your sampling strategy first: if you run maj@16 the accuracy spread nearly vanishes and the decision collapses to total token cost, and the model that samples cheapest wins. We compared these same models on a very different axis, long horizon tool use, in the Fable 5 versus Grok 4.3 agentic coding benchmarks.

If your team is putting a model behind a task with exact answers and needs pass@1, self consistency, and cost per solve measured on your own problem distribution rather than a public set, Contra Collective builds and evaluates these reasoning systems with the token bill priced into the acceptance criteria. The leaderboard rewards the highest score; your pipeline has to afford it every time it runs.

FAQ

Which model is most accurate on AIME 2026 in July 2026? On a single attempt, Claude Fable 5 at 91 percent pass@1, with Grok 4.3 and GPT-5.5 a point or two behind. Under self consistency (maj@16) all three reach the mid 90s and the accuracy spread compresses to about a point, so the practical answer depends on whether you sample once or vote.

Why does cost per solved problem differ so much if accuracy is close? Because the models spend very different amounts of reasoning to reach similar accuracy. GPT-5.5 uses a median of about 9,800 tokens per problem, Fable 5 about 14,200, and Grok 4.3 about 18,600. Since you pay per token, near identical accuracy comes at meaningfully different bills, and self consistency multiplies that gap by the sample count.

What is self consistency and when should I use it? Self consistency samples the model several times and takes the majority answer. Use it when a wrong result is expensive enough to justify the extra token spend. On AIME it lifts all three models into the mid 90s, which is the right approach for high stakes math where correctness outweighs cost and latency.

Which model is safest to run unattended? It depends on your check. Fable 5's failures are usually a single late arithmetic slip that a verifier or a vote catches, making it safe with light guarding. GPT-5.5's early case pruning produces confident wrong answers that need self consistency or a structural review to catch. Grok 4.3 mainly risks running past its token cap, which a higher limit fixes at the cost of more tokens.

Does a higher AIME score mean a better math model for production? Not on its own. Two models within a point on accuracy can differ sharply on token cost, latency, and failure mode, and those decide whether you can run the model at volume and trust it unsupervised. Read pass@1 alongside cost per solve and the failure profile, not in isolation.