AI Hallucination in 2026: Which LLM Hallucinates the Most While Coding?

AI hallucinations in software are hardest to catch when the code looks correct. It may compile, follow the expected structure, and explain itself clearly — and the risk only appears later, when review exposes a missing security check, shallow test coverage, a false assumption, or unfinished business logic.

In METR’s early-2025 trial, experienced open-source developers expected AI tools to reduce task time by 24%. Instead, task time increased by 19%. The added cost came from verification.

TechnBrains tested seven LLMs across four coding tasks and scored each output using the same hallucination-risk framework: Claude Sonnet 4.6 Max, Gemini 3.5 Flash, Gemini 3.1 Pro, GLM, Kimi K2.6 Instant, MiniMax M3, and Codex 5.5 Medium. These are the models our AI development team uses in real workflows — but final validation still sits with engineers before code moves toward production.

Quick Verdict

• GLM had the highest average hallucination risk in our test, at 4.38 out of 10.
• Kimi was the riskiest in implementation-heavy tasks, scoring 6 out of 10 on both the Next.js S3 upload and the password reset.
• Claude had the lowest overall risk at 1.75, with the strongest structure and security.
• MiniMax was the strongest non-Claude performer at 1.88 — full-project outputs and real self-verification.

The table below shows each model’s hallucination-risk score across all four coding tests. A lower score means lower risk.

What Is an AI Hallucination?

A coding hallucination happens when a model presents something false as working code. That can mean an invented SDK method, a fake package, the wrong framework pattern, a non-existent config option, or a security claim the code does not actually support.

A normal bug uses a real tool incorrectly. An AI hallucination often invents the tool, behavior, or production safety around it. In practice, this looks like “production-ready” code with TODO business logic, a password reset flow that claims token hashing but stores raw tokens, a test suite that claims full coverage while missing combined logic, or a webhook that returns success before processing is complete.

That is why AI-built prototypes need engineering review before they become real products. We observed the same risk when we tested the different AI app builders, where the gap between a working demo and launch-ready software is the main issue.

How We Tested Each LLM for Coding Hallucinations

We ran the same task through each model, reviewed every output by hand, and scored it on the same framework. Same prompt for every model, a fresh chat each time. We did not correct the model mid-test, we saved screenshots and full outputs, and where a model ran its own tests we noted what its screenshots showed.

This is a point-in-time engineering test, not a permanent ranking. Behavior changes with version, settings, context window, tools, retrieval, and prompt design. Scoring runs 0 (no issue) to 10 (confidently wrong, fake, broken, or unsafe), across six factors: fake reference, runtime or implementation risk, framework mismatch, security omission, weak test confidence, and confidence gap.

Round 1: Which LLM Handled Stripe Webhooks Safest?

Prompt: A Node.js Express webhook handler for Stripe subscriptions, with signature verification, required events, environment variables, proper responses, and error handling.

None of the models clearly invented a fake Stripe API. The stronger outputs understood raw request-body handling and stripe.webhooks.constructEvent(). Claude and MiniMax were strongest because they waited for handler execution before responding and used safer error behavior. The main risk was confidence inflation — models called webhook shells production-ready while leaving provisioning, access revocation, dunning, database sync, idempotency, monitoring, and retry-safe processing as TODOs.

Lesson: Round 1 revealed no fake Stripe APIs. The risk was confidence inflation — shells called production-ready while idempotency, retries, monitoring, and persistence were missing.

Round 2: Which LLM Built the Safest Next.js S3 Upload Route?

Prompt: A Next.js TypeScript App Router route that accepts JPG, PNG, and WebP only, limits size to 5MB, uploads to S3, returns the file URL, uses environment variables, and avoids credential exposure.

No model fell back to the old Pages Router — every model put the route under app/api/upload/route.ts. The differences were in production safety. Claude, MiniMax, and Codex produced the cleanest implementations. Kimi carried the highest risk because it hand-rolled a fragile multipart parser even though App Router supports request.formData().

Lesson: Framework choice was solid. The risk moved into hardening — env validation, empty-file handling, and not assuming a public URL resolves.

Round 3: Which LLM Handled Password Reset Security Best?

Prompt: A password reset flow with Express, Prisma, and PostgreSQL — a secure reset token, safe storage, expiry, anti-enumeration, a reset endpoint, password hashing, rate limiting, environment variables, Prisma fields, and a security explanation.

This round exposed the clearest gap between secure-sounding code and actually safer code. Claude and MiniMax produced the strongest security architecture: hashed tokens, expiry, one-time use, and rate limiting. GLM and Kimi looked advanced but added complexity, copy-paste risk, or weaker race-safety than their explanations claimed.

Lesson: The dangerous failure was not missing token generation — most models got that right. The risk was confidence around incomplete rate limiting, email-failure behavior, and race-safety. If you build payment, auth, or multi-role logic, this is where custom software development and engineering review earn their place.

Round 4: Which LLM Wrote the Most Reliable Jest Tests?

Prompt: Jest tests for a pricing function, covering normal cases, quantity, discount behavior, cart total above 500, empty cart, and missing-validation risks.

This round exposed false coverage confidence. Claude and MiniMax performed best because they treated it like a QA review, not a checklist. Gemini 3.5 Flash was weakest, covering basics but few edge cases.

Lesson: AI-generated tests can look complete while staying shallow. The best models tested rule interaction, rounding, and bad inputs — not just the obvious cases.

Final Ranking: Which LLM Hallucinated the Most?

A lower score means lower AI hallucination risk. This is a point-in-time engineering test across four prompts, not a universal leaderboard.

To be precise: GLM had the highest average risk score, so by the numbers it hallucinated the most. Kimi was the riskiest in the implementation-heavy rounds specifically. Both readings matter, and they point at different failure modes.

What Is the AI Confidence Gap in AI-Generated Code?

The confidence gap is the distance between how finished code sounds and how finished it is. It was the biggest failure pattern in our test — bigger than fake APIs.

OpenAI’s 2025 research ties this to training that rewards confident guessing over admitting uncertainty. Dario Amodei, at Anthropic’s Code with Claude event in May 2025, framed the upside and the catch in one line: AI models “probably hallucinate less than humans, but they hallucinate in more surprising ways.”

What Is AI Sycophancy, and How Does It Affect Code?

Sycophancy is when a model optimizes for your approval instead of the truth. It is the same incentive behind the confidence gap, pointed at your ego. In April 2025, OpenAI rolled back a GPT-4o update it called “overly flattering or agreeable” — a behavior it described as sycophantic — after new reward signals based on user thumbs-up weakened the checks that held it down. Sam Altman called the update “too sycophant-y and annoying” before the rollback.

We saw the coding version of this in every round. Models declared shells production-ready, claimed full coverage, and described security the code did not implement. None of it was hostile — it was eager. The fix is not a better mood from the model. It is review that does not care how confident the output sounds.

What Do Developers Say About AI Hallucinations?

Developer sentiment is not “AI is useless.” It is sharper than that: the common complaint is that AI makes code faster to produce and slower to trust.

Reddit discussions around AI coding show the same pattern.

In r/technology, engineers describe being handed AI-generated code that technically works but still needs cleanup, review, restructuring, and de-risking before it belongs in a real codebase. The frustration is not that AI cannot generate code — it is that the review burden moves to senior engineers after the code already looks finished.

In r/vibecoding, non-developers ask the harder question: how do you know the AI is hallucinating if you cannot read the code? The strongest answers point back to the same controls we used in this test — small tasks, code review, tests, official docs, and someone technical enough to know what “clean” means.

In r/BlackboxAI_, the mood is more pragmatic. Developers do not reject AI coding tools outright — they treat hallucination as the cost of speed. The safer workflow is narrow tasks, small diffs, test coverage, and asking the model to explain changes before accepting them.

Why Did AI Hallucinations Become a 2026 Search Trend?

Public cases moved AI hallucinations from theory to professional risk. In April 2026, the law firm Sullivan & Cromwell apologized to a New York bankruptcy judge after an AI-assisted filing carried roughly 40 fabricated or wrong citations. A 2026 audit by Zhao and colleagues estimated 146,932 hallucinated citations in scientific papers published in 2025 alone.

The searches around this fall into a pattern: people look for an “AI checker” to detect problems (but AI checkers flag AI-written text, not incorrect code), for an “AI agent” to reduce errors (grounded agents help, weak agents automate mistakes faster), and for general “generative AI and LLM hallucination” explanations, since hallucination is a broad generative-AI problem.

How Should Software Teams Verify AI-Generated Code?

AI coding tools speed up work, but they are not production engineers. The stakes are rising: Microsoft’s Satya Nadella said 20–30% of code in some company repos is now written by AI, and Google’s Sundar Pichai said more than 25% of new code at Google is AI-generated and then reviewed by engineers. More AI code means more review, not less.

Practical checks from the test:

• Verify API references against official docs.
• Run the generated code.
• Check the framework version and routing patterns.
• Review secret handling, token storage, and expiry.
• Validate rate limiting on every sensitive endpoint, not just the obvious one.
• Check whether tests cover rule interaction, not only happy paths.
• Treat “production-ready” claims as unverified.
• Require senior engineering review before merge.

A generated feature is not launch-ready software. Closing that gap is the work a mobile app development team does after the model stops.

The Bottom Line

No model in this test clearly invented fake Stripe or AWS APIs. The bigger risk was harder to catch: code that sounded finished while security, persistence, business logic, or test depth was still missing.

GLM had the highest average risk score at 4.38. Kimi was riskiest in implementation-heavy rounds, scoring 6 on both the upload and password reset tests. Claude had the lowest risk at 1.75, with MiniMax close behind at 1.88. The ranking matters less than the pattern: the weakest outputs were confident about unfinished work, and that confidence gap is what makes AI-generated code dangerous in production. AI can write the first draft. Verification still belongs to the engineering team.