GPT-5.6 Sol vs Grok 4.5: Aider Polyglot Multi Language Code Editing Tested (August 2026)

A model that aces greenfield code generation can still be a poor coding agent, because the agent's real job is editing code that already exists and returning that edit in a format a tool can apply on its own. Aider Polyglot is built to test that skill and not the other one. It takes Exercism problems across six languages, gives the model an existing file, and requires a targeted edit that both passes a hidden test suite and lands as a diff the harness applies cleanly. Get the logic right but the diff format wrong and you fail the problem, which is exactly what happens inside a real agent loop when a malformed edit forces a retry. Here is the comparison first, then the language and failure mode split that decides which model to actually build on.

Headline Comparison

Dimension	GPT-5.6 Sol	Grok 4.5
Type	Proprietary API	Proprietary API
Context window	400K tokens	256K tokens
Input price, per 1M tokens (list)	$4.00	$3.00
Output price, per 1M tokens (list)	$16.00	$12.00
Aider Polyglot overall pass@1, our run	79.1	76.4
Correct edit format rate	98.9%	96.2%
C++	74	73
Go	80	75
Java	81	78
JavaScript	83	82
Python	86	84
Rust	71	66

The overall gap is under three points, which at this resolution is real but modest. The pricing gap is wider in percentage terms than the accuracy gap, and that alone reframes the choice for anyone running these at volume. The averages, though, hide the part that matters for an agent, which is the edit format rate and the per language spread. Read those two rows before you read the headline.

How We Ran It

One attempt per problem, no retries, a fixed system prompt, and the diff based edit format for both models so the comparison is apples to apples. Every solution was applied by the harness and run against the exercise's hidden tests; a problem counts as passed only if the tests go green after the model's edit applies. We logged two failure classes separately: a logic failure, where the edit applied cleanly but the tests failed, and a format failure, where the edit itself did not apply because the model returned a malformed or misaligned diff. Keeping those two apart is the whole point, because they have different fixes and very different costs inside an agent.

We did not average across multiple runs to smooth variance, because the goal is a single clean read of one attempt per problem, which is the condition an agent actually operates under when it takes its first shot at an edit. Temperature was held low for both models to favor deterministic edits. Prices are list rates at time of testing and move often, so treat the cost columns as a snapshot, not a fixed quote.

Where the Two Models Separate

The per language rows tell the useful story. Both models are strongest on Python and JavaScript, which is unsurprising given how much of both languages sits in any training corpus, and the two are within a point or two of each other there. The gap widens on Go and Java, where GPT-5.6 Sol pulls ahead by several points, and it widens most on Rust, where GPT-5.6 Sol leads by five. Rust is the sharpest discriminator in the whole suite, because its borrow checker punishes edits that a more forgiving language would accept, and a model has to reason about ownership and lifetimes to produce an edit that compiles at all. A model that is merely pattern matching common code shapes gets caught by Rust in a way it does not by Python.

That points at a practical read. If your codebase is Python and JavaScript, these two models are close enough that price and latency should decide, and Grok 4.5 is meaningfully cheaper. If your work is systems code in Rust or backend services in Go and Java, GPT-5.6 Sol's lead is large enough to matter, because each failed edit in those languages is a retry, and retries are where an agent's real cost and wall clock time accumulate. The averages would tell you these models are nearly tied; the language split tells you the tie only holds for some stacks.

The Edit Format Rate Is the Quiet Decider

The row most people skip is the one that matters most for agentic use: correct edit format rate. GPT-5.6 Sol returned an applyable diff on 98.9% of problems, Grok 4.5 on 96.2%. That looks like a small gap until you translate it into agent behavior. Every format failure is a wasted round trip: the model produced an answer, the harness could not apply it, and in a real agent the loop now has to detect the failure, re prompt, and pay for a second generation. At 96.2% roughly one edit in twenty six fails on format alone before logic is even evaluated, against one in ninety for the higher rate, and in a long autonomous run those failed applies compound into latency and token spend that never shows up in a pass rate average.

This is why edit format compliance is a first class metric for agents and an afterthought for chat. In a chat window a human sees the malformed diff and fixes it in a second. In an agent loop there is no human, so a format failure is a full retry, and a model that is a fraction of a point better on raw problem solving but noticeably worse on format can be the more expensive model to operate. Function calling reliability sits right next to this concern, and we measured that separately in the BFCL function calling comparison; the through line is that structured output compliance, not raw capability, is what makes or breaks autonomous operation.

Reading This Against the Other Benchmarks

Aider Polyglot is a code editing benchmark, and it should not be read alone. A model's ability to reason about a change and its ability to edit an existing file are related but not identical, and the fuller picture comes from triangulating across suites. We put GPT-5.6 Sol against a different pairing on this same benchmark in the Opus 5 vs GPT-5.6 Sol Aider Polyglot run, which is worth reading if Opus is in your candidate set, and we measured Grok 4.5's broader coding and reasoning profile in the LiveBench comparison. Grok 4.5 looks stronger on open ended reasoning tasks than its Polyglot edit rate suggests, which fits a pattern: it reasons well and formats a touch less reliably, a trade that favors it for interactive work and disfavors it for hands off agents.

The composite read is that these are both capable coding models with different sweet spots. GPT-5.6 Sol is the safer pick for an autonomous agent editing a polyglot or systems heavy codebase, where its format reliability and Rust and Go lead reduce retries. Grok 4.5 is the value pick for interactive coding in mainstream languages, where a human catches the occasional format slip and the lower price compounds in your favor across a day of use.

When This Applies to Your Stack

Pick by how the model will run, not by the headline number. If you are wiring a model into an agent that edits code without a human watching each diff, weight the edit format rate and the languages you actually ship, because retries are the real cost and Rust and Go are where they cluster. GPT-5.6 Sol is the stronger choice there. If a developer is in the loop and your code is mostly Python and JavaScript, Grok 4.5 gives you nearly the same pass rate at a lower price, and the format gap is something a human absorbs without noticing. Run the benchmark against your own repositories before committing, because your language mix and edit patterns will shift these numbers, and a public leaderboard cannot see your codebase.

Choosing and integrating a coding model into a real agent loop, with the retry handling, format validation, and cost controls that keep an autonomous run from quietly burning tokens, is the AI integration and agent engineering work Contra Collective does. The benchmark tells you which model to start with; the engineering around it decides whether the agent is dependable or merely impressive in a demo.

FAQ

What does Aider Polyglot actually measure? It measures whether a model can edit existing code across six languages, C++, Go, Java, JavaScript, Python, and Rust, and return the change as a diff that applies cleanly and passes a hidden test suite. Unlike generation benchmarks it tests editing, which is what a coding agent spends most of its time doing, and it separately tracks whether the edit arrived in a valid format.

Which model won overall? GPT-5.6 Sol led on our run at 79.1 pass@1 against Grok 4.5 at 76.4, a gap under three points overall. The more decisive differences are GPT-5.6 Sol's higher edit format rate and its larger lead in Rust and Go, while Grok 4.5 stays within a point or two in Python and JavaScript at a lower price.

Why does the edit format rate matter so much? Because every format failure is a wasted round trip. In a chat a human fixes a malformed diff instantly, but in an autonomous agent there is no human, so a bad edit forces the loop to detect the failure and generate again. A model that formats edits reliably has fewer retries, which means lower latency and lower token spend in a long run, even if its raw pass rate is only slightly higher.

Is Grok 4.5 a bad choice given the lower numbers? No. Grok 4.5 is the value pick for interactive coding in mainstream languages, where its lower price compounds across heavy daily use and a developer catches the occasional format slip. Its reasoning profile is strong, as its LiveBench results show. The case against it is narrow: hands off agents editing Rust, Go, or Java heavy code, where the retry cost of format and language slips outweighs the price saving.

How should I use these numbers for my own project? As a starting shortlist, not a verdict. Weight the languages you actually ship and how the model will run, agent or assisted, then run the benchmark against your own repositories, because your code patterns will move the results. Prices listed are a snapshot and change frequently, so recheck current rates before committing to a model at volume.