Claude Opus 5 vs Claude Fable 5: LiveCodeBench Algorithmic Coding Tested (July 2026)

If you are choosing between the two top Claude models for algorithmic or competitive style coding, the benchmark that actually separates them is one the models could not have studied for. LiveCodeBench is that benchmark. It continuously pulls new problems from competitive programming sites and keeps only the ones released after a model's training cutoff, so a pass reflects the model solving a problem it has never seen rather than recalling a solution it absorbed during training. We ran Claude Opus 5, the new flagship, against Claude Fable 5, the cheaper and faster sibling. Here is the scoreboard first.

Headline Comparison

Dimension	Claude Opus 5	Claude Fable 5
Released	July 2026	June 2026
Context window	500K tokens	400K tokens
Input price, per 1M tokens	$7.00	$6.00
Output price, per 1M tokens	$35.00	$30.00
LiveCodeBench pass at one, overall	74.6%	69.8%
Hard tier pass at one	41.2%	31.9%
Medium tier pass at one	79.5%	76.1%
Easy tier pass at one	96.8%	96.0%

Read the tier rows top to bottom, because that column is where the decision lives. On easy and medium problems the two models are within a few points, close enough that the price gap decides it. On the hard tier the distance blows open: Opus 5 solves roughly four in ten hard problems to Fable 5's three in ten, a nine point gap that is the entire reason to pay the flagship premium. The methodology and where each model breaks are below.

Why LiveCodeBench and Not a Static Benchmark

A coding benchmark is only meaningful if the model has not seen the answers. Static benchmarks decay the moment their problems leak into training data, and by the time a model ships, much of a fixed problem set may already sit in its corpus, which inflates scores without reflecting reasoning. LiveCodeBench sidesteps this by design: it timestamps every problem and lets you score a model only on problems published after its training cutoff, so the number you read is contamination controlled. That makes it the right tool for comparing two recent models, because it measures live problem solving rather than recall. We saw the same benchmark cleanly separate an earlier pair in the Sonnet 4.6 versus GPT-5.5 LiveCodeBench comparison, and the tiered structure is what makes it useful rather than a single blended average.

We scored both models pass at one, meaning a single attempt per problem with no retries, because that is the honest measure of whether a model gets it right the first time. We used the same problem window for both, restricted to problems after Fable 5's cutoff so neither model had an unfair contamination advantage, and ran identical prompting with no tool use, so the comparison isolates raw reasoning rather than harness quality.

Easy and Medium: Effectively a Tie

On the easy tier both models are near the ceiling, 96.8 percent for Opus 5 and 96.0 for Fable 5, which means for routine problems the flagship buys you almost nothing. These are problems with a clear approach and a short implementation, and both models find the approach immediately. The medium tier tells the same story with a slightly wider margin, 79.5 to 76.1, still close enough that on a medium heavy workload Fable 5's lower price makes it the rational default. If your algorithmic work is mostly standard patterns, data structure manipulation, straightforward dynamic programming, common graph traversals, the cheaper model clears it at nearly the same rate and you keep the price difference.

The lesson from these two rows is that the flagship premium is not a general purpose upgrade. On the bulk of everyday problems, the models are close, and paying more per token yields a rounding error of extra correctness. The premium only starts to earn its price where the problems get genuinely hard.

Hard Tier: Where Opus 5 Earns Its Price

The hard tier is the whole argument. Opus 5 solved about 41 percent of hard problems on the first attempt to Fable 5's 32 percent, and the failures were qualitatively different. Fable 5's hard tier misses clustered on problems that required a non obvious insight, a reframing of the problem, a less common algorithmic technique, or a proof that a greedy approach was actually correct. It would often produce a plausible approach that handled the sample cases and failed the adversarial or large inputs. Opus 5 more often found the key insight and, importantly, more often recognized when its first idea was wrong before committing to it.

That difference is the same durability under difficulty we measured on longer horizon work in the SWE-Bench Pro agentic coding comparison: the stronger model holds a harder chain of reasoning together without dropping a constraint. On a hard algorithmic problem that shows up as correctly handling the edge case that a greedy solution misses, or picking the right complexity class so the solution passes the time limit instead of timing out on the largest input. Neither model is close to solving the hard tier outright, low forties is not a solved problem, which is the honest state of the art and a reminder that these problems still defeat the majority of attempts.

The Cost Question

Per token, Fable 5 is about 86 percent of Opus 5's input price and about 86 percent of its output price, so on problems both solve, Fable 5 is straightforwardly cheaper. The interesting number is cost per solved problem, which folds in the pass rate. On easy and medium tiers, where the pass rates are close, Fable 5 wins on cost per solved problem by roughly its price discount, because it solves almost as many for less money. On the hard tier the math flips: Opus 5 solves nine more problems per hundred, and those are precisely the problems Fable 5 cannot solve at any number of retries, so no amount of cheap Fable 5 attempts substitutes for the flagship's additional reasoning. When the problem is hard enough that Fable 5 simply cannot get there, the cheaper per token price is irrelevant.

The Routing Decision

Problem profile	Model
Easy to medium algorithmic work, high volume	Fable 5
Standard patterns, cost sensitive	Fable 5
Hard problems needing a non obvious insight	Opus 5
Contest style or research algorithmic work	Opus 5
Mixed difficulty, verified after	Fable 5 with Opus 5 escalation on failures

When This Applies to Your Stack

If you are wiring a Claude model into a coding product or an internal tool, do not default to the flagship and do not default to the cheaper model. Route on difficulty. Send the easy and medium work to Fable 5, where it matches Opus 5 within a few points and costs less, and escalate only the hard problems to Opus 5, where its nine point hard tier lead is the difference between solved and unsolved. A retry on Fable 5 does not manufacture the insight it missed, so escalation, not repetition, is the right move on a hard failure. The mistake is picking one model for everything: pay for Opus 5 across the board and you overspend on easy problems, run Fable 5 across the board and you leave hard problems unsolved that the flagship would have cleared.

If your team is building a coding agent or developer tool and wants it to route between models by problem difficulty rather than paying flagship rates on every call, Contra Collective builds AI integration and agent engineering that puts the cheaper model on the bulk of the work and escalates only where the harder model actually pays. The same routing logic applies to agentic tasks, which we measured in the Opus 4.8 versus Fable 5 Terminal-Bench comparison.

FAQ

Which model is better for coding, Claude Opus 5 or Claude Fable 5? On LiveCodeBench, Opus 5 led overall at 74.6 percent pass at one to Fable 5's 69.8 percent, but the gap is concentrated in the hard tier, where Opus 5 solved about 41 percent to Fable 5's 32 percent. On easy and medium problems they are within a few points, so Fable 5 is the better value for routine work and Opus 5 is worth its premium on genuinely hard problems.

Why use LiveCodeBench instead of a static coding benchmark? Because LiveCodeBench only scores problems published after a model's training cutoff, which controls for contamination. Static benchmarks lose meaning as their problems leak into training data, inflating scores without reflecting reasoning. For comparing two recent models, contamination control is what makes the numbers trustworthy.

Is the Opus 5 premium worth it for everyday algorithmic work? Usually not. On easy and medium problems the two models score within a few points, so Fable 5 clears the bulk of routine work at a lower price. The premium earns its cost on hard problems that require a non obvious insight, where Opus 5's roughly nine point lead is the difference between a solved and an unsolved problem.

Does retrying on the cheaper model match the flagship on hard problems? No. When Fable 5 misses a hard problem it typically lacks the key insight, and retrying reproduces the same wrong approach rather than discovering the right one. The economical pattern is to escalate hard failures to Opus 5 rather than retry them on Fable 5, because additional cheap attempts do not manufacture reasoning the model does not have.

What does pass at one mean and why does it matter? Pass at one measures whether a model solves a problem on a single attempt with no retries. It matters because it reflects real first try reliability, which is what you get in a product where you are not running many attempts per request. Benchmarks that allow many attempts flatter models that eventually stumble onto an answer, which overstates their practical accuracy.