Claude Sonnet 5 vs Qwen 3.6: CRUXEval Code Reasoning Tested (July 2026)

If you are choosing a model to sit inside an agent that reads code before it edits, the benchmark that predicts how it behaves is not the one that grades generation. It is the one that asks the model to predict what a function does. CRUXEval is that benchmark: it gives the model a short program and asks it to reason about execution, either predicting the output from an input or predicting an input that yields a given output. We ran Claude Sonnet 5, a mid tier frontier model, against Qwen 3.6, the strongest open weight model you can self host today. Here is the result first.

Headline Comparison

Dimension	Claude Sonnet 5	Qwen 3.6
Type	Proprietary API	Open weight, self hostable
Context window	500K tokens	256K tokens
Input price, per 1M tokens	$3.00	Self host or about $0.60 hosted
Output price, per 1M tokens	$15.00	Self host or about $2.40 hosted
CRUXEval output prediction, pass@1	89.4%	84.1%
CRUXEval input prediction, pass@1	86.7%	82.9%
Failures concentrated in	Deep nested state	Loops and integer edges

Read the two pass rates together with the price row, because that is the actual decision. Claude Sonnet 5 leads by roughly five points on output prediction and four on input prediction, a real but not enormous gap, while Qwen 3.6 costs a fraction as much to run and can be hosted on your own hardware where no tokens leave your network. The question the rest of this post answers is whether those few points of reasoning accuracy matter for your use, and where each model actually breaks.

What CRUXEval Measures

CRUXEval is built from small, self contained Python functions paired with input and output examples. The output prediction task gives the model the function and an input and asks what the function returns. The input prediction task gives the function and an output and asks for an input that produces it. Both are graded by actually executing the code, so there is no partial credit for a plausible looking answer that is wrong. The tasks are short by design, which isolates reasoning from retrieval and long context handling: the model is not being tested on whether it can find the relevant code, only on whether it can simulate execution once the code is in front of it.

That isolation is why the benchmark is a good proxy for a specific agent skill. When an agent reads a function before modifying its caller, it is doing output prediction in its head. When it reasons backward from a desired result to the input that would cause it, it is doing input prediction. A model that scores well here is more likely to correctly understand the code it is about to change, which is the failure mode that produces confident, wrong edits.

Where Each Model Breaks

The headline numbers hide the more useful finding, which is that the two models fail differently. Claude Sonnet 5's errors cluster in functions with deep nested state: dictionaries of lists mutated across several steps, closures capturing variables that change, and cases where the answer depends on tracking an object through many transformations. When it fails, it usually loses the thread partway through a long mutation chain rather than misreading a single operation.

Qwen 3.6's errors cluster earlier, in loop bound arithmetic and integer edge behavior: off by one boundaries, negative indexing, integer division and modulo with negative operands, and the exact iteration count of a loop with a nontrivial range. These are the cases where simulating execution requires careful, mechanical tracking rather than pattern recognition, and the frontier model's extra reasoning depth shows up as fewer of these slips. For an agent, that difference matters because loop and index errors are exactly the kind of subtle bug that passes a quick read and fails in production, so the model that makes fewer of them is safer to trust with an unsupervised edit.

The Cost Argument for the Open Weight Model

The reason this comparison is interesting rather than a foregone conclusion is cost. Qwen 3.6 is open weight, which means you can run it on your own hardware and pay nothing per token, or use a hosted endpoint at roughly a fifth of Sonnet 5's price. In an agent loop that reads dozens of functions per task, code reasoning calls are frequent and cheap individually but add up, so a fivefold price difference on the reasoning tier is not trivial. If your agent uses a strong model for planning and editing but needs a cheaper model for the high volume read and comprehend steps, Qwen 3.6 at 84% output prediction may be entirely adequate for the reading tier while a frontier model handles the edits. We explored the same split from the generation side in the Qwen 3.6 versus GPT-5.5 agentic tooling comparison.

Usage pattern	Claude Sonnet 5	Qwen 3.6
High volume code reading in an agent loop	Accurate but costs add up	Cheap, accuracy usually sufficient
Unsupervised edits that depend on exact behavior	Safer, fewer subtle slips	Riskier on loops and index math
Self hosted, no tokens leave the network	Not available	Native advantage
Mixed: cheap model reads, strong model edits	Strong edit tier	Strong read tier

Which to Choose

The honest split is about how much you trust the model unsupervised and how much you care about token cost. If your agent makes edits with little human review and those edits depend on precisely understanding existing behavior, Claude Sonnet 5's few extra points of reasoning accuracy are worth paying for, because the failures it avoids are the expensive ones. If you are running a high volume comprehension workload, summarizing code, answering questions about a codebase, or feeding a cheaper read tier into a pipeline where a stronger model does the actual editing, Qwen 3.6 is the pragmatic pick and self hosting removes the per token cost entirely. Neither answer is wrong; the deciding factor is whether the marginal reasoning accuracy prevents failures that would cost you more than the token savings. For multi file editing rather than pure reasoning, our Opus 5 versus GPT-5.6 Sol Aider Polyglot test covers the edit side, and the SWE-Bench frontier leaderboard tracks the agentic end.

When This Applies to Your Stack

If you are building an agent that reads code, do not choose the model on a generation benchmark and assume comprehension comes along for free. They are related but not identical, and CRUXEval measures the one that governs whether your agent understands what it is about to change. Decide which steps in your loop are high volume reads and which are high stakes edits, and consider a mixed setup where a cheap, self hostable model handles the reading and a frontier model handles the edits, rather than paying frontier prices for every comprehension call or trusting a cheaper model with edits it is not reliable enough to make. The mistake we see most often is teams picking one model for the whole loop, either overpaying for cheap steps or under provisioning the expensive ones.

If your team is putting a coding model inside a real agent and wants the read tier and edit tier chosen and wired against your actual codebase, Contra Collective does AI integration and agent engineering that treats the model split, the tool interface, and the review policy as one system, so the agent understands the code before it touches it.

FAQ

What does CRUXEval test that generation benchmarks do not? Code reasoning rather than code writing. CRUXEval gives the model a short function and asks it to predict the output from an input, or an input that produces a given output, and grades the answer by actually executing the code. It measures whether the model understands what a program does when it reads one, which is a different and often weaker skill than producing code that passes tests.

Is Claude Sonnet 5 or Qwen 3.6 better at code reasoning? Claude Sonnet 5 scored higher in our CRUXEval runs, leading by roughly five points on output prediction and four on input prediction. Qwen 3.6 is close and costs a fraction as much, and it can be self hosted. Sonnet 5 is the safer pick for unsupervised edits that depend on exact behavior; Qwen 3.6 is the pragmatic pick for high volume comprehension where its accuracy is usually sufficient.

Why does code reasoning matter for coding agents? Because an agent reads code before it changes it. Predicting what a function returns is exactly what an agent does when it inspects a call site before editing, and reasoning backward from a result to an input is what it does when it debugs. A model that misreads execution will make confident, wrong edits that break the callers depending on the behavior it misunderstood.

Can I self host Qwen 3.6 for this? Yes. Qwen 3.6 is open weight, so you can run it on your own hardware with no per token cost and no tokens leaving your network, or use a hosted endpoint at roughly a fifth of Sonnet 5's price. That cost advantage is the main reason to consider it for the high volume reading steps in an agent loop, where reasoning calls are frequent.

Should I use one model or two in my agent? For many agents a two model split is the efficient answer: a cheap, self hostable model like Qwen 3.6 handles high volume code reading and comprehension, and a frontier model like Claude Sonnet 5 handles the high stakes edits that depend on precise reasoning. Using a single model for the whole loop tends to either overpay for the cheap steps or under provision the expensive ones.