Best AI in 2026? ChatGPT, Kimi, DeepSeek and 3 More Tested

30-second guide: choose by task, not score alone

For a tighter two-product decision, continue with ChatGPT vs Claude, ChatGPT vs Gemini, Claude vs Gemini, or DeepSeek vs Kimi.

Test environment setup

All six products used free accounts on the same MacBook, macOS 26.5.1, Chrome 150, and the same Los Angeles network exit. Each task started in a new conversation, except the multi-turn task, which continued from the writing conversation.

We report exactly what each product displayed instead of equating ChatGPT, Claude, or DeepSeek with a specific API model. Chat products may route requests dynamically, and access may change by account, region, capacity, and release date. The official product pages also warn that free limits and availability can change: ChatGPT plans, Claude Pro regional pricing, and Gemini Apps limits.

Scoring: quality and speed are separate

The 100-point rubric assigns 12 points to Chinese writing, 10 to JSON extraction, 10 to constraint planning, 14 to web research, 16 to TypeScript code generation, 14 to React debugging, 12 to PDF reading, 7 to chart interpretation, and 5 to multi-turn editing.

JSON, dates, calculations, schedules, and chart values were checked against answer keys. Code was inspected for concurrency limits, rejection handling, cancellation, races, and HTTP errors. Writing and research were judged on factual boundaries, structure, and source discipline. Latency, subscriptions, quotas, and regional access are reported separately and do not affect quality scores.

For third-party review, we published the nine complete prompts, both test assets, fixed-case answer keys, the archived scoring rubric, per-case scores, a 54-cell product-by-case evidence index, and sanitized screenshots in a GitHub reproducibility package. The package also states its limits: the 54 full copyable responses were not preserved, and the detailed rubric was documented after the run rather than preregistered. The archive improves traceability, but it does not turn this test into a fully reproducible experiment.

These are not intelligence scores, and they exclude latency and peak availability. Kimi had the highest answer-quality score but some of the longest waits. DeepSeek and GLM-5.2 tied at 69 for entirely different reasons.

Writing, JSON extraction, and planning: the basics barely separate them

Chinese writing

All six products preserved the source material’s main point: users should not choose an AI solely from a leaderboard. None used the prohibited buzzwords or invented prices. Strict format compliance separated them:

• ChatGPT produced four subheadings instead of exactly three and missed the 320–380 Chinese-character range.
• Claude’s original response was clear and natural but too short. Because its original incognito conversation was not recoverable, the later multi-turn test used a fresh consecutive writing-plus-revision run; that replacement first turn was too long.
• DeepSeek wrote the richest answer, but it was substantially over length and used four subheadings.
• Gemini used three subheadings but was under length.
• GLM-5.2 and Kimi met the length, title, three-subheading, analogy, and two-action requirements. Kimi earned full credit but spent several minutes revising before delivery.

The practical lesson is that good prose and specification compliance are different capabilities. For CMS fields, ads, and fixed-length summaries, compliance can matter more.

JSON extraction

All six returned valid JSON with the correct dates, booleans, budget allocation, owners, and null deadline. No product added prose outside the JSON. This was the cleanest tie in the test.

Constraint planning

ChatGPT, Claude, DeepSeek, GLM-5.2, and Kimi produced workable schedules; ChatGPT, GLM-5.2, and Kimi received full credit. Claude duplicated an opening line, while DeepSeek invented meeting names and a date. Gemini Flash-Lite made substantive errors: its table split both A and B despite their continuity requirements, duplicated time for A, then proposed a different corrected schedule in the verification section without updating the table. It scored 4/10.

View full-size image

×

Gemini Flash-Lite planning table contradicting its verification

Figure 1: Gemini noticed that B should not cross lunch but left the invalid table unchanged; A, C, and idle time also differed between the table and verification.

Web research: none of the six AI products fully passed

The research task allowed only official OpenAI, Google, Anthropic, and DeepSeek domains and required at least one direct source per company row. It exposed the difference between research-looking output and verifiable research.

View full-size image

×

ChatGPT Free web-research answer

Figure 2: ChatGPT linked official evidence for its OpenAI row, but the other rows lacked direct official citations and contained information that was stale by the test date.

• ChatGPT: 4/14. The OpenAI row had clickable official links. The Google, Anthropic, and DeepSeek rows did not satisfy the per-row source rule, and older model names were presented as current.
• Claude: 2/14. It displayed many links, but the Google row relied on a non-official publication and several model or event claims were not supported by the cited official pages.
• DeepSeek: 1/14. It searched 15 pages, including Yahoo, an AI wiki, cloud-vendor blogs, and aggregators, directly violating the domain restriction. It also promoted predictions and unofficial reports into its conclusions.
• Gemini: 4/14. Every row had an official link, but the core results were badly stale for the test date, still naming GPT-4o, Gemini 1.5 Pro, Claude 3.5 Sonnet, and DeepSeek-V3/R1.
• GLM-5.2: 0/14. Agent mode failed twice under load. After switching to the recommended GLM-5-Turbo, the agent generated irrelevant code and its web_search tool timed out without producing the requested table.
• Kimi: 7/14. Ordinary web mode eventually produced a four-row table restricted to official-looking domains, but took about three minutes. Several URLs were plain text rather than clickable citations, Anthropic’s team page did not directly support the model and pricing claims, and some prices remained approximate.

View full-size image

×

Kimi official-source research table

Figure 3: Kimi produced the most complete research result, but the evidence still did not close every claim-to-source loop.

The citation count is therefore a poor proxy for research quality. Each statement still needs three checks: is the domain allowed, does the linked page support the claim, and is the date the event date rather than the article or crawl date?

Coding: TypeScript generation and React debugging split the field

The TypeScript task requested a dependency-free mapLimit with a strict concurrency ceiling and first-error behavior. DeepSeek, Gemini, GLM-5.2, and Kimi preserved input order, validated limit synchronously, stopped launching new work after the first rejection, and handled started promises correctly. All four earned 16/16.

ChatGPT and Claude were mostly correct, but both waited for already-running work before rejecting the outer promise instead of rejecting as soon as the first error was observed. Claude’s counter logic was also more complicated. They scored 13/16 and 12/16.

The React hook task reversed the result:

• ChatGPT, Claude, GLM-5.2, and Kimi added the query dependency, empty-query short circuit, encodeURIComponent, AbortController, response.ok check, and stale-response protection. All scored 14/14.
• Gemini covered dependencies, encoding, cancellation, non-2xx responses, and AbortError, but relied on AbortController without a separate stale-result guard. A narrow cleanup race remained, so it scored 12/14.
• DeepSeek canceled the old request when a new non-empty request began, but its empty-query branch returned before aborting the previous controller. An earlier request could still update state after the query became empty, so it scored 9/14.

PDF reading: Kimi, ChatGPT, and DeepSeek fully passed; GLM mixed contexts

The PDF was an eight-page synthetic project report. The products had to cite pages while finding metrics, costs, methodology changes, a P0 incident, rollout stop conditions, and a savings calculation.

ChatGPT, DeepSeek, and Kimi correctly found:

• June Web auto-resolution of 74.2% and citation coverage of 95.0% on page 3;
• the largest one-time item, model inference and load testing, at CNY 186,000 or 39.1%, on page 4;
• a cache key missing tenant_id, potentially affecting 43 answers, with a 2-hour-15-minute pause, on page 6;
• CNY 211,200 in gross labor savings minus CNY 83,200 in operating costs, yielding CNY 128,000 per month, excluding the one-time investment and its amortization.

View full-size image

×

ChatGPT Free PDF answer

Figure 4: ChatGPT supplied the correct values, calculation, and PDF page references.

ChatGPT, DeepSeek, and Kimi met the full specification. Claude’s facts and citations were correct, but its required 120–160-character conclusion was substantially too long, costing two points.

Gemini found values such as 74.2%, 95.0%, CNY 186,000, 39.1%, and CNY 128,000, but claimed the cost-item name was not in the document, reduced a 2-hour-15-minute outage to “15,” and omitted that the savings excluded one-time investment and amortization. It scored 7/12. GLM-5.2 was worse: although the correct filename was visible, the response described an unrelated “Safe Train 6724” project, earning 0/12.

View full-size image

×

Gemini PDF answer missing important qualifiers

Figure 5: Gemini found most headline numbers but missed the cost label, incident units, and excluded-cost clause.

Chart reading: DeepSeek produced the most serious hallucination

The chart showed Team A and Team B completion rates from January through June and marked a switch to evaluation set v2 beginning in April. The correct answers were A=71% and B=67% in March, A=82% and B=79% in June, a pre-change maximum month-over-month increase of six points for Team A from January to February, and a June relative lift of about 3.8%.

ChatGPT, Claude, Gemini, and Kimi read every item correctly and separated visible facts from calculated conclusions.

View full-size image

×

Gemini chart answer

Figure 6: Gemini correctly read all four key values, the largest monthly increase, the 3.8% relative lift, and the evaluation-set change.

View full-size image

×

DeepSeek Free chart answer

Figure 7: DeepSeek copied the four requested headline values correctly, but invented a nonexistent Team C and claimed the chart did not identify the methodology change.

This is more dangerous than a simple arithmetic mistake: the answer is detailed and confident, yet the supporting series does not exist. Anyone using AI for financial charts, experiments, or operating dashboards should explicitly check for invented series, labels, and annotations.

View full-size image

×

Claude Free chart answer

Figure 8: Claude correctly identified the April evaluation-set change and explained why March-to-April values were not directly comparable.

GLM-5.2 correctly read June and calculated the three-point gap and 3.8% relative lift, but misread March as A=68% and B=63% and confused a cumulative increase with a one-month increase. It scored 4/7. Kimi passed all seven checks.

Multi-turn editing: only Gemini and Kimi fully passed

The second turn changed the audience from first-time users to enterprise AI procurement leads, removed the everyday analogy, retained three subheadings, added a testing-fairness or version-risk warning, and reduced the target to 180–220 Chinese characters.

All six preserved the facts, removed the analogy, avoided new rankings and prices, and included a version warning. Counting the title, subheadings, and body together:

• ChatGPT produced about 244 characters;
• Claude produced about 301;
• DeepSeek produced about 308.
• Gemini produced 199 characters and Kimi 211; both retained exactly three subheadings and fully passed.
• GLM-5.2 preserved the content and risk warning but did not clearly retain three subheadings, scoring 3/5.

Only Gemini and Kimi fully passed. A conversation can retain the topic while still failing to reconcile old and new hard constraints. Length, schema, and template requirements should be verified programmatically after generation.

Kimi, GLM-5.2, and agent mode: capability plus availability

Kimi and GLM-5.2 use the same tasks and scoring logic as the other products. The evaluation also asks whether the advertised agent or deep-research workflow can actually start on a free account during peak demand.

GLM-5.2: excellent text and code, unacceptable PDF context failure

Z.ai’s ordinary Chat mode earned full credit on writing, JSON, planning, TypeScript generation, and React debugging. The writing response met the length, three-subheading, analogy, and action requirements. Both code answers correctly handled concurrency, rejection, cancellation, races, and non-2xx responses. Its multi-turn revision preserved the facts, changed the audience, and added a version-risk warning, but did not clearly preserve three subheadings, scoring 3/5.

The file test was the critical failure. The interface correctly displayed case07_northstar_report.pdf, yet GLM-5.2 produced a wholly unrelated completion report for a “Safe Train 6724: Three Sheep” project, including 82 knowledge-base documents and four roles that did not exist in the PDF. This was not a minor page or transcription error; the entire context belonged to another subject, so the PDF case scored 0/12.

View full-size image

×

GLM-5.2 answering the correct upload with an unrelated project

Figure 9: The uploaded filename is correct, but the answer is unrelated to the test document. A context mix-up this severe should not be softened into a passing score.

The chart result was mixed. GLM-5.2 correctly read June as A=82% and B=79%, calculated a three-point gap and roughly 3.8% relative lift, and noticed the April switch to evaluation set v2. It misread the clearly labeled March values as A=68% and B=63%, then treated a January-to-March cumulative change as a one-month increase. It scored 4/7.

View full-size image

×

GLM-5.2 chart answer

Figure 10: GLM-5.2 computed the June gap and relative lift correctly but misread March as A=68% and B=63% instead of A=71% and B=67%.

Kimi: highest answer quality, with latency as the main weakness

Kimi completed all nine cases. It received full credit for writing, JSON, planning, both code tasks, PDF reading, chart interpretation, and multi-turn editing. It found every required cross-page PDF fact and kept the visible-versus-inferred chart distinction intact. Its only material deduction came from research, where the final table was structurally complete but not every source was clickable or directly supported the model, date, and price in the same row.

The waiting experience was unstable. Writing took more than three minutes of repeated recounting before the final draft arrived. TypeScript, React, PDF, chart, and multi-turn tasks also took roughly one to three minutes in several runs. The product displayed a peak-capacity notice and switched to K2.6 Quick. The 93/100 score therefore means the strongest final answers, not the best product for deadline-sensitive work.

View full-size image

×

Kimi remaining in its reasoning trace during the writing test

Figure 11: Kimi had already drafted usable copy but continued recounting and renaming terms. The final answer was correct, but delivery was slow.

Agent mode: many entry points do not guarantee execution

Both products expose extensive agent surfaces. Z.ai’s Agent mode lists chat apps, full-stack development, intelligent writing, data insights, and Feishu or WeChat connections. Kimi lists Agent Swarm, Deep Research, Documents, Websites, Sheets, Kimi Work, Kimi Code, and Kimi Claw.

With the same official-sources-only research prompt, Z.ai Agent returned a peak-load error and asked the user to wait or switch to GLM-5-Turbo. After the switch, its web_search tool still timed out. Kimi Deep Research reported that too many users were chatting and that subscribers could enter a priority queue; ordinary web chat eventually completed the table. Agent entry points received no separate bonus, and the research case was scored only on delivered output.

View full-size image

×

GLM-5.2 Agent unavailable under peak load

Figure 12: The Z.ai Agent reported heavy model usage, and the research task failed to start twice.

View full-size image

×

Kimi Deep Research blocked by a peak-time queue

Figure 13: Kimi Deep Research reported heavy chat load and offered members a priority queue; the free account had to fall back to ordinary web chat.

View full-size image

×

GLM-5-Turbo Agent search tool timing out

Figure 14: After switching to GLM-5-Turbo, the agent's `web_search` tool timed out after 30 seconds and never delivered the table.

The practical conclusion is straightforward: agent quality is not only the number of tools on a menu. It also includes whether the run starts, whether queues are acceptable, whether an uploaded file reaches the correct context, and whether a failed job offers a clear recovery path. Under the observed free-tier peak conditions, Kimi needed ordinary chat as a fallback, while GLM did not complete the research task.

Latency observations: approximate timings on one machine

The user timed the first four cases manually. Later desktop cases used approximate browser timestamps, which include network variation, queues, uploads, rendering, and automation waits. The table therefore compares experience ranges rather than forcing a precise six-product speed ranking.

DeepSeek was fastest on short text tasks, ChatGPT had the best balance of latency and stability, and Kimi traded much longer waits for the highest answer-quality score. Gemini fell back from Pro to Flash-Lite after the multi-turn case, showing that free quota is itself an experience variable. For agents, latency also includes whether a task starts and eventually delivers, not only generation time.

Which of the six AI products fits you

Choose ChatGPT first when

You want one product for files, charts, debugging, and iterative edits, with fewer obvious failures. It was the most consistent product here, but web research still needs source verification and Free access remains capacity- and feature-limited. For the line-by-line differences, see ChatGPT vs Claude and ChatGPT vs Gemini; plans and access are covered on the ChatGPT tool page.

Choose Claude first when

You value readable Chinese explanations and careful React or TypeScript analysis. Its PDF and chart facts were accurate. Its weakness was verbosity under fixed-length constraints, so add a character counter or schema check when output size matters. Further reading: Claude tool page, Claude vs Gemini.

Choose DeepSeek first when

Your workload is Chinese Q&A, structured extraction, scheduling, or standalone algorithm generation, and speed matters. This run does not support using Quick mode alone for source-restricted research or high-stakes chart interpretation. Further reading: DeepSeek tool page, DeepSeek vs Kimi, DeepSeek vs Qwen.

Choose Gemini first when

You need chart interpretation, code generation, and strict multi-turn rewriting, and can tolerate an automatic fallback after the free quota. Its 73 points were pulled down by Flash-Lite’s planning errors and the incomplete PDF answer. Complex schedules should be checked for agreement between the table and the verification section. Further reading: Gemini tool page, Claude vs Gemini.

Choose Kimi first when

You prioritize final-answer quality over turnaround time and can wait through longer reasoning. Kimi topped the quality rubric at 93 and was excellent on PDF, charts, code, and revision. Peak fallback, a queued Deep Research mode, and minute-scale waits make it a poor sole option for urgent work. Further reading: Kimi tool page, DeepSeek vs Kimi, Kimi vs Doubao.

Choose GLM-5.2 first when

Your workload is primarily text, structured extraction, and code. The Z.ai and GLM-5.2 tool page explains how its chat, agent, file, and coding surfaces fit together. It was nearly flawless on text and code here, but the PDF context mix-up means file handling should be checked first, and the agent may still fail under peak load.

How to rerun this test: five steps and three common traps

1. Fix the account tier, device, network exit, model selector, thinking mode, and search toggle.
2. Start each task in a new chat, except the designated multi-turn task.
3. After uploading a file, verify its filename and completed upload state before timing.
4. Preserve raw responses; do not repair a product’s facts or formatting before scoring.
5. Keep pricing, quotas, latency, and capability separate, and do not treat a chat product as identical to its API.

This article publishes the test environment, all nine task weights, per-task scores, key answer checks, and 14 screenshots showing passes or failures. The GitHub package additionally contains the full prompts, assets, answer keys, rubric, machine-readable scores, 18 surviving evidence images, and an explicit gap list. The screenshots document the product states observed in this run; they are not a complete archive of every conversation. Treat the scores as an explainable snapshot from July 30, 2026 under the stated free-account and interface conditions, not as a permanent ranking that applies to every account, region, or later release. A rerun should preserve the constraints and answer checks, not merely reuse the task topics.

The most common mistakes are equating a product name with a foundation-model name, assuming that more citation badges mean better research, and ranking latency across different devices and networks.

Final verdict: which AI to pick in 2026

The unified quality ranking is Kimi 93, ChatGPT 82, Claude 75, Gemini 73, DeepSeek 69, and GLM-5.2 69. Kimi won on completeness, ChatGPT on balance, Claude on readable explanations and debugging, and Gemini on charts, code, and revision while showing weaker planning stability after free-tier fallback. DeepSeek stood out for speed and standalone code, and GLM-5.2 for text and code with a serious file-context caveat.

These results are affected by network routing and variability, account permissions, the underlying model selected by each product, model versions, usage limits, and peak-load conditions. The same prompt may produce different results in another region, at another time, or under a different account or model route. The ranking therefore represents one comparison in the stated environment on July 30, 2026. It is a reference for product selection and reruns, not a permanent conclusion that applies to every user.

If I had to give a default recommendation to most users, it would still be ChatGPT. It did not top the content score, but it avoided GLM’s file-context failure, DeepSeek’s chart hallucination, and Kimi’s repeated multi-minute waits. If deadlines are flexible and research citations will be checked manually, Kimi produced the strongest final answers. The safer workflow is not to crown one universal winner, but to use one primary tool for routine work and a second product to verify high-risk research, document, and chart conclusions.

Frequently Asked Questions (FAQ)

Q: Which AI is best in 2026?

By capability total across the nine tasks in this round, Kimi scored highest at 93, followed by ChatGPT 82, Claude 75, Gemini 73, DeepSeek 69, and GLM-5.2 69. "Highest score" and "best to use" are not the same thing: Kimi took one to three minutes on several tasks and automatically downgraded to K2.6 Quick at peak load. If you want one product to open by default, ChatGPT was the most balanced on quality, speed, and stability, and it produced none of the high-risk failures such as file-context mix-ups or chart hallucinations.

Q: Why test only the web products and not the APIs?

Because this round answers a different question: which chat product web app is more reliable for an ordinary user, not which underlying model is strongest. Chat products route models dynamically and change with account, region, capacity, and release timing. In this round Gemini fell back to Flash-Lite once the free quota ran out, and Kimi switched to K2.6 Quick at peak. Those are real parts of the web experience that never appear in an API benchmark. That is why the article records what the product interface displayed rather than equating a product name with a specific API model, and evaluates APIs and chat sites separately.

Q: Kimi scored highest, so why recommend ChatGPT?

Because the capability score excludes speed and peak-load availability. Kimi's 93 means its final answers were the most complete, but the writing task took more than three minutes of repeated revision, and the TypeScript, React, PDF, chart, and multi-turn tasks each waited one to three minutes. Its Deep Research mode also failed in a queue. ChatGPT's 82 is lower, yet it avoided GLM's PDF context mix-up, DeepSeek's chart hallucination, and the minute-scale waits. Pick ChatGPT when work has a deadline; pick Kimi when waiting is acceptable and sources will be checked by hand.

Q: Which product is most reliable for PDFs and charts?

On the PDF task (12 points), ChatGPT, DeepSeek, and Kimi all scored full marks, locating the figures, page numbers, and exclusion clauses such as "one-off investment and amortization not included" across pages. Claude was factually correct but overran the summary length for 10, Gemini missed the cost label and incident units for 7, and GLM-5.2 scored 0 after answering about an unrelated project. On the chart task (7 points), ChatGPT, Claude, Gemini, and Kimi were all correct, while DeepSeek invented a Team C that does not appear in the chart and scored 2. Taking both together, Kimi and ChatGPT were the most dependable.