AI Hallucinations 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. The risk appears later, when review exposes a missing security check, shallow test coverage, 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 LLMs our AI development team uses in real workflows, but final validation still sits with engineers before code moves toward production.

Quick Verdict

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

What Is an AI Hallucination?

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 can look 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, fresh chat each time.
• We did not correct the model mid-test.
• We saved screenshots and full outputs.
• We reviewed each output against the framework: API references, framework patterns, security behavior, test quality, and the confidence gap.
• 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 We Used: A Node.js Express webhook handler for Stripe subscriptions, with signature verification, required events, environment variables, proper responses, and error handling.

In the Stripe webhook test, 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 across the round was confidence inflation: models called webhook shells production-ready while leaving subscription provisioning, access revocation, dunning, database sync, idempotency, monitoring, and retry-safe processing as TODOs. 

GLM had the riskiest response behavior, acknowledging the webhook before business processing was complete.

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 Hallucinates the Most When Building a Safe Next.js S3 Upload Route?

Prompt We Used: 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. 

Gemini 3.5 Flash and GLM gave workable but thinner outputs with weak environment validation. Kimi carried the highest risk because it hand-rolled a fragile multipart parser even though App Router supports request.formData(). Between the Gemini models, 3.1 Pro edged 3.5 Flash, which leaned on empty-string credential fallbacks.

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 We Used: 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. Gemini 3.1 Pro gave a clean baseline. 

Gemini 3.5 Flash handled the core token flow but missed reset-endpoint rate-limit coverage. 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 Hallucinates Least When Writing Reliable Jest Tests?

Prompt We Used: 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 While Coding? 

1. Claude (1.75), lowest overall risk. Strongest structure and security, with caveats included rather than hidden.
2. MiniMax (1.88), strongest non-Claude performer. Full-project outputs and real self-verification in testing.
3. Codex (2.75), clean and practical, less deep. Good shells and security, lighter edge coverage.
4. Gemini 3.1 Pro (3.38), more reliable than Flash overall. Solid concepts, weaker packaging.
5. Gemini 3.5 Flash (3.88), fast and clean but thinner on deeper security and testing.
6. Kimi (4.25), riskiest in implementation-heavy tasks. Deep test coverage, but overcomplication drove a 6 and 6 in the build rounds.
7. GLM (4.38), highest average hallucination risk. Good ideas undercut by messy, copy-paste-risky execution.

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? 

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?

In April 2025, OpenAI rolled back a GPT-4o update it called “overly flattering or agreeable,” a behavior it described as sycophantic. 

The company said new reward signals based on user thumbs-up had weakened the checks that held sycophancy down, so the model skewed toward answers that were supportive but disingenuous. 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 in Coding? 

The 2025 Stack Overflow Developer Survey shows the same split. AI adoption reached 84%, but only about 3% of developers said they highly trust AI output. More developers distrust AI accuracy than trust it, and the top frustration, cited by 66%, was AI answers that are “almost right, but not quite.

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, so people started searching for what it is and how to catch it.

The legal example: 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:

• AI checker: people want detection, but AI checkers flag AI-written text. They do not verify whether code is technically correct.
• AI agent: grounded agents with tools reduce some errors, but weak agents automate mistakes faster.
• Generative AI and LLM hallucination: 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% to 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. 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.