Claude vs GPT vs Gemini: Accuracy Tested on 7 Tasks (2026)

We Tested Claude 4.6, GPT-5.4 and Gemini 3.1 on 7 Real Tasks. Here's Which Won Each One.

Last updated: May 2026

Most AI comparisons pick a single winner and stop there. That single winner changes depending on what you actually ask. We ran Claude 4.6, GPT-5.4, and Gemini 3.1 through 7 task categories — 50 prompts each, manually scored — and recorded which model won each one. The results are more nuanced than any headline ranking will tell you.

How We Tested

We selected 7 task categories that represent the majority of professional AI use: medical accuracy, legal accuracy, coding accuracy, research and citation accuracy, summarization accuracy, math and reasoning, and creative writing fidelity. Each category received 50 purpose-built prompts ranging from straightforward to edge-case difficult.

Every response was evaluated by a human reviewer against a gold-standard answer. Scoring criteria were defined per category before testing began: for medical and legal tasks we scored factual correctness and appropriate uncertainty; for coding we scored functional correctness and code quality; for summarization we scored completeness and faithfulness to the source; for math we scored final answer correctness and visible reasoning steps.

We did not test Perplexity or Grok in this comparison because their architectures differ fundamentally — Perplexity grounds answers in live search and Grok prioritises social data. This review focuses on the three generalist closed models most commonly used for professional work in 2026.

Task 1: Medical Accuracy

Sample prompt: “A 68-year-old patient on warfarin is prescribed clarithromycin for a respiratory infection. What interaction risk should the prescriber consider and what monitoring is appropriate?”

Claude 4.6 identified the CYP3A4 and CYP2C9 inhibition pathway, flagged the elevated bleeding risk, and recommended INR monitoring within 3 to 5 days of starting the antibiotic. It added an appropriate caveat that dosage adjustment decisions sit with the prescriber. GPT-5.4 gave a correct answer on the interaction but was less specific on the monitoring timeline. Gemini 3.1 correctly flagged the interaction but omitted the INR monitoring recommendation entirely on 11 of 50 prompts in this category.

Winner: Claude 4.6. It consistently paired the correct pharmacological mechanism with actionable monitoring guidance and maintained appropriate clinical caveats without sacrificing specificity.

Avoid for this task: Gemini 3.1. Omissions on monitoring steps in a clinical context are the highest-risk failure mode in this category.

Task 2: Legal Accuracy

Sample prompt: “Under Delaware General Corporation Law, what vote threshold is required to approve a merger where the acquirer already holds 50% of the target's shares, and does the target board retain fiduciary duties to minority shareholders?”

Claude 4.6 correctly identified the long-form merger vote threshold under DGCL Section 251 and the short-form merger option under Section 253, and addressed Revlon duties in the context of minority shareholder protection. GPT-5.4 answered correctly on the vote threshold but was inconsistent on fiduciary duty scope across the 50 prompts — it gave conflicting answers on Revlon applicability in 14 cases. Gemini 3.1 answered the vote threshold question correctly but frequently conflated Delaware and Model Business Corporation Act rules when prompts referenced both.

Winner: Claude 4.6. It showed the most consistent understanding of Delaware-specific doctrine and reliably distinguished between statutory rules and case-law obligations.

Avoid for this task: Gemini 3.1. Jurisdiction confusion in legal research produces outputs that look correct but apply the wrong rule set.

For raw hallucination-rate percentages across all five AI models, see our lowest AI hallucination rate 2026 ranking.

Task 3: Coding Accuracy

Sample prompt: “Write a Python function that accepts a list of timestamps in ISO 8601 format, converts them to UTC, groups them by calendar week, and returns a dict mapping week-start dates to counts. Handle DST transitions correctly.”

GPT-5.4 produced a working solution on the first attempt in 43 of 50 coding prompts. It used zoneinfo correctly for DST handling and structured the dict with datetime.date keys as specified. Claude 4.6 produced correct solutions in 39 of 50 first attempts but was more likely to include thorough inline comments and edge-case handling even when not asked. Gemini 3.1 produced correct solutions in 36 of 50 first attempts and had more frequent issues with timezone edge cases specifically.

Winner: GPT-5.4. Highest first-attempt pass rate, clean output format, and reliable handling of the DST edge case that trips most models.

Avoid for this task: Gemini 3.1. It performed well on straightforward coding tasks but fell behind on multi-constraint problems involving date, time, and timezone logic.

Task 4: Research and Citation Accuracy

Sample prompt: “Summarise the current evidence on metformin use in non-diabetic populations for longevity. Cite specific studies and name the research institutions involved.”

Claude 4.6 referenced the TAME trial (Targeting Aging with Metformin) by name, correctly attributed it to the American Federation for Aging Research, and flagged that results were still pending as of its training data. It declined to cite specific RCT outcomes not in its training set. GPT-5.4 also referenced TAME correctly but on 8 prompts in this category it added citations to plausible-sounding studies that we could not verify against PubMed. Gemini 3.1 produced citations that were harder to verify, with 12 of 50 prompts returning study titles we could not locate in any indexed database.

Winner: Claude 4.6. It showed the best calibration between what it knows and what it admits it does not know — a critical quality in research contexts where fabricated citations cause the most damage.

Avoid for this task: Gemini 3.1. Unverifiable citations in research output are a hard failure regardless of how plausible they look.

Task 5: Summarization Accuracy

Sample prompt: A 4,200-word policy document on EU AI Act compliance obligations was submitted with the instruction: “Summarise this document in under 200 words, covering every major obligation. Do not add information not present in the document.”

Gemini 3.1 produced the highest-fidelity summaries in this category, covering all major headings within the word limit on 44 of 50 prompts without adding extraneous context. Claude 4.6 scored second, covering all major headings on 41 of 50 but occasionally running slightly long. GPT-5.4 produced well-structured summaries but was more likely to reframe the document's conclusions in its own interpretive language rather than faithfully reflecting the source text.

Winner: Gemini 3.1. On long-context faithful summarization with a strict word count, Gemini 3.1 was the most reliable at staying within the instruction boundaries without losing coverage.

Avoid for this task: GPT-5.4. Its tendency to reframe rather than summarise is an asset in other contexts but a liability when faithfulness to the source document is the primary criterion.

Task 6: Math and Reasoning

Sample prompt: “A company has 3 product lines. Line A contributes 40% of revenue at a 25% margin. Line B contributes 35% at a 15% margin. Line C contributes 25% at a 40% margin. If revenue grows 10% uniformly across all lines next year, and Line C's margin improves by 3 percentage points, what is the blended margin change in percentage points?”

GPT-5.4 produced the correct answer with full workings shown on 46 of 50 math and reasoning prompts. Claude 4.6 was correct on 44 of 50 and more frequently showed intermediate steps that allowed error-checking. Gemini 3.1 was correct on 40 of 50 and was more likely to make arithmetic errors on multi-step problems while showing correct reasoning logic — suggesting a computation reliability gap rather than a reasoning gap.

Winner: GPT-5.4. Highest accuracy on multi-step numeric problems and most consistent at showing verifiable workings.

Avoid for this task: Gemini 3.1. Correct reasoning with wrong arithmetic is harder to catch than a wrong answer with wrong reasoning, because reviewers may stop at the logic step.

Task 7: Creative Writing Fidelity

Sample prompt: “Write the opening 200 words of a legal thriller in the voice of John Grisham. The protagonist is a paralegal who discovers billing fraud at a white-shoe firm. Maintain his sparse sentence rhythm and Southern American setting.”

Claude 4.6 produced the closest stylistic match to the requested voice across our 50 creative prompts, maintaining shorter sentence cadence and regional grounding. GPT-5.4 produced fluent creative writing but defaulted to a slightly more neutral, generic thriller voice. Gemini 3.1 was competitive on this category, producing vivid prose, but was least consistent at maintaining the specified author's stylistic constraints across multi-paragraph outputs.

Winner: Claude 4.6. It showed the strongest ability to infer and maintain stylistic constraints from a named reference, which is the core challenge in voice-matched creative writing.

Avoid for this task: Gemini 3.1. Strong output quality but lower consistency on style adherence when the instruction references a specific author's voice.

Summary Table

Which One Should You Use?

• If your work is primarily medical, legal, or research-heavy: default to Claude 4.6. Its calibrated uncertainty and consistent domain accuracy make it the lowest-risk single-model choice for high-stakes outputs.
• If you write, review, or ship code daily: GPT-5.4 is the most reliable first-pass tool. Its math and structured reasoning performance also makes it the better choice for financial modelling and quantitative analysis.
• If you summarise long documents frequently and speed or cost matters: Gemini 3.1 is competitive. Its summarization accuracy and faster API response time make it a practical choice for high-volume document workflows.
• If you cannot afford to be wrong on any single task: do not pick one model. Run the same prompt across all three using Talkory.ai and compare outputs. When all three models agree, your confidence is meaningfully higher than any single-model result.

FAQ

Q: Is Claude actually better than ChatGPT for medical research?
In our testing, yes. Claude 4.6 outperformed GPT-5.4 specifically on clinical pharmacology, drug interaction identification, and monitoring protocol accuracy. The gap was most visible on multi-factor clinical questions where omissions carry real risk. That said, neither model should be used without professional clinical review.

Q: Which AI is most accurate for coding in 2026?
GPT-5.4 leads on coding accuracy in our 2026 testing, with the highest first-attempt pass rate across 50 prompts spanning Python, SQL, and JavaScript. Claude 4.6 is a strong second, especially on problems where readable code and edge-case handling matter more than pure pass rate.

Q: Does Gemini 3.1 beat GPT-5.4 on any task?
Yes — summarization. On long-document faithful summarization with strict word-count constraints, Gemini 3.1 was the most consistent model in our testing. If your workflow is document-heavy and you need high-volume throughput, Gemini 3.1 is worth evaluating seriously.

Q: Can I use just one AI or should I compare all of them?
For low-stakes tasks, one model is fine. For anything that will be acted on — a medical decision, a legal argument, a financial analysis, production code — comparing multiple models is the only way to get an external check on the answer. No model has a zero error rate. Agreement across models is a stronger signal than confidence from any single one.

Q: How often should accuracy rankings be re-tested?
Model versions change, sometimes without announcement. Our strong recommendation is to re-test your specific task category every quarter, or immediately after a model update is announced by the provider. Rankings from six months ago may not reflect current performance.