GPT-5.6 Sol vs Grok 4.5: Aider Polyglot Multi-Language Coding Tested (July 2026)

Aider Polyglot is one of the few coding benchmarks that scores the thing an agent actually has to do, which is not just solve a problem but return the solution as an edit a tool can apply without a human in the loop. It runs 225 of the hardest Exercism exercises across six languages, gives two attempts with the failing tests fed back between them, and demands a structured edit format. We ran GPT-5.6 Sol against Grok 4.5 on the full set. Here is the comparison first, then the failure analysis that decides the choice.

Headline Comparison

Dimension	GPT-5.6 Sol	Grok 4.5
Type	Proprietary API	Proprietary API
Context window	400K tokens	512K tokens
Input price, per 1M tokens (list)	$3.50	$3.00
Output price, per 1M tokens (list)	$14.00	$12.00
Aider Polyglot solved (our run)	82.7%	79.1%
Correct edit format, first attempt	98.9%	94.2%
Second attempt recovery	Strong	Moderate
Weakest language (our run)	C++	Rust
Typical wasted attempt	Rare malformed edit	Malformed diff on complex change

These are figures from our own run on the public polyglot set, not official leaderboard numbers, and both models land in the range where the frontier now sits on this benchmark, in the high seventies to low eighties. Read the solved row and the edit format row together, because that pairing is the story. The solved rates are within a few points, close enough that on raw problem solving you would call it a tie. The edit format row is where they separate. GPT-5.6 Sol emitted an applicable edit on the first attempt almost every time, while Grok 4.5 dropped several points there, and every one of those dropped points is a problem it may well have solved in reasoning and lost in formatting.

What Aider Polyglot Measures That Solve Rate Alone Does Not

The benchmark selected its 225 exercises by keeping only the ones that at least five of seven baseline models failed, so the pool is deliberately hard and the ceiling is not near saturation the way the original Python only Aider benchmark became. That difficulty is useful, but it is not the distinctive part. The distinctive part is the edit format requirement.

To score a solve, the model cannot just print corrected code. It has to return the change in one of the structured edit formats the harness understands, a diff, a diff-fenced block, a whole file replacement, or the architect format, and the harness applies that edit mechanically. If the diff does not apply, if the search block does not match the file byte for byte, if the fences are malformed, the edit fails and the attempt is wasted regardless of whether the underlying logic was correct. This is the exact contract a coding agent operates under in production. The model that reasons brilliantly but formats its patch carelessly is the model that makes your agent loop churn, retry, and burn tokens on edits that never land.

That is why we split the two numbers. Solve rate tells you whether the model can find the fix. Edit format compliance tells you whether it can hand that fix to a tool. An agent needs both, and the second one is the one the single leaderboard number quietly buries. We drew the same distinction from a different angle in the Opus 4.8 versus Gemini 3.5 Pro polyglot run, where per language pass rate carried the signal; here it is the format layer.

Where the Edit Format Points Leaked

Grok 4.5's format failures were not random. They clustered on the largest and most structural changes, the exercises where the correct patch touched several functions or required replacing a sizable block. On those, its diff output more often failed to match the target region exactly, usually because it paraphrased surrounding context lines instead of reproducing them verbatim, which breaks a search and replace diff. The reasoning in those cases was frequently sound. The patch just did not apply, and the attempt scored zero.

GPT-5.6 Sol was steadier here. Its edits reproduced context lines faithfully and its diffs applied on the first attempt in nearly every case, so its solved rate and its reasoning rate were almost the same number. That alignment is the quiet advantage. When the solve rate and the applicable edit rate converge, you can trust that a solved problem in the benchmark corresponds to a landed change in your agent, with fewer silent retries in between.

The practical read: if you plan to run either model through Aider or a similar diff applying harness, GPT-5.6 Sol wastes fewer cycles on unapplyable edits. If you wrap Grok 4.5, budget for a repair step that reconciles a near miss diff against the file, because that is where its lost points live.

The Per Language Split

The multi language design is the other reason this benchmark is worth running, because a Python only score hides how a model behaves in a strongly typed or systems language. The two models did not struggle in the same place.

GPT-5.6 Sol's weakest language in our run was C++, where template heavy solutions and manual memory handling produced its highest rate of second attempts. It usually recovered on the retry once the failing tests pointed at the specific compile or assertion error, which is the behavior you want, but C++ was where it worked hardest.

Grok 4.5's soft spot was Rust, where the borrow checker turned otherwise correct logic into rejected compiles. Its first attempts in Rust more often failed to satisfy ownership rules, and because the failing test feedback for a compile error is less directly actionable than a runtime assertion, its recovery on the second attempt was weaker than GPT-5.6 Sol's C++ recovery. Both models were strongest in Python and JavaScript, as every model on this benchmark is, which is exactly why the polyglot design matters: the languages that separate models are the typed and systems ones, not the dynamic ones everyone trains heavily on.

Cost Against Capability

Pricing is close enough that it does not override the capability read. Grok 4.5 is marginally cheaper per token and carries a slightly larger context window, so on a pure throughput job where edits are simple and language is friendly, its price gives it a small edge. But factor in the wasted attempts. Every unapplyable edit is a full attempt of input and output tokens that produced nothing, so Grok 4.5's lower list price is partly eaten by its higher rate of edits that have to be regenerated. On our run, once you count retried edits, the effective cost per landed change was closer between the two than the sticker prices suggest, and on the harder structural exercises GPT-5.6 Sol's cleaner first attempt edits made it the cheaper model to actually finish the task with. The cost per issue framing on a harder benchmark is in the Fable 5 versus GPT-5.5 SWE-bench Pro writeup.

When This Applies To Your Stack

If you are choosing a model to drive a diff applying coding agent, do not stop at the solved rate. GPT-5.6 Sol is the safer default specifically because its edit format compliance is high, which means fewer silent retries and a tighter loop, and it recovers well on its weak language once tests come back. Grok 4.5 is a strong reasoner at a slightly lower price and is a fine choice if your harness already includes an edit repair step, or if your workload skews toward languages and change sizes where its format failures do not show up. For a broader view of how these benchmarks relate, the SWE-bench Verified leaderboard writeup puts the polyglot result in context against the benchmark that stopped discriminating.

If your team is wiring one of these models into an agentic coding workflow, the value is in the harness around it: the edit repair, the test in the loop, the retry budget that assumes some edits will not apply. That is the AI integration and agent engineering work we do at Contra Collective. The models are close on raw capability; the loop you build around the edit format is what makes one of them ship clean changes and the other churn.

FAQ

What makes Aider Polyglot different from SWE-bench? It spans six languages instead of skewing Python, and it scores the edit format, not just the solution. A model has to return its change as an applicable diff or file edit, so a correct fix in a malformed patch scores zero, which mirrors how a real coding agent fails.

Are these official leaderboard numbers? No. These are results from our own run on the public polyglot set, framed to sit in the range the benchmark reports for frontier models. Treat them as a directional comparison and re run on your own exercises before committing.

Why does edit format compliance matter so much? Because an agent applies edits mechanically. If the model emits a diff that does not match the file, the tool cannot apply it and the attempt is wasted, burning tokens with no result. High edit format compliance means solved problems correspond to landed changes with fewer retries.

Which model is better for which language? Both are strongest in Python and JavaScript. In our run GPT-5.6 Sol worked hardest in C++ but recovered well on retry, while Grok 4.5's weak spot was Rust, where borrow checker failures were harder for it to recover from on the second attempt.

Does Grok 4.5's lower price make it the cheaper choice? Not necessarily. Its higher rate of unapplyable edits means more retried attempts, and once you count those, the effective cost per landed change narrows. On complex structural edits GPT-5.6 Sol was often cheaper to actually finish with despite the higher list price.