Updated August 2026

    Which AI model should you actually use?

    Research Monkey's Multi-Model Chat gives you ChatGPT, Gemini, Claude, DeepSeek, Perplexity and Grok in one thread. They are not interchangeable. This guide explains what each family is genuinely good at, where it falls down, and how the individual models inside each family differ — so you can pick deliberately instead of by habit.

    11 min read
    By Research Monkey AI

    The 30-second answer

    Hard reasoning, analysis or codeGPT-5.6 Sol, Claude Opus 5 or Gemini 3.1 Pro
    Everyday drafting and editingClaude Sonnet 5 or GPT-5.6 Terra
    Long PDFs, transcripts and big contextGemini 3.6 Flash or Gemini 2.5 Pro
    Anything current or that needs citationsPerplexity Sonar Pro (or Sonar Deep Research)
    Breaking news and social sentimentGrok 4
    Maths, proofs and step-by-step logicDeepSeek R1 or V4 Pro
    Cheap, high-volume batch workGemini 3.1 Flash-Lite, GPT-5.4 nano or DeepSeek Chat
    Chinese-language workDeepSeek V4 Pro

    One rule of thumb worth internalising: only Perplexity and Grok read the live web. Every other model answers from training data plus whatever you attach, so for anything time-sensitive you either use one of those two or paste the source in yourself.

    ChatGPT

    OpenAI · The dependable generalist

    OpenAI's GPT family is the most broadly capable line in the picker: it is strong at reasoning, writing, coding, structured output and image understanding, and it is the safest default when you do not know in advance what a task will demand. The GPT-5 generation is a tiered family — a flagship, a balanced mid-tier and a fast small model — so the practical decision is less "which vendor" and more "how much reasoning does this task deserve".

    Pros

    • Most consistent instruction-following across very different task types
    • Strong at long, structured deliverables that must hold a format
    • Wide tier range, so you can trade cost against depth without switching vendor
    • Reliable with citations and quotations when you paste the source text in

    Cons

    • Flagship and Pro tiers are among the most expensive per token
    • Reasoning tiers can be noticeably slower than Flash-class models
    • Prose can read cautious and hedged compared with Claude
    • No live web access on its own — it answers from training data plus what you attach

    Best for

    • Complex, multi-step reasoning and analysis
    • Structured output (tables, JSON, outlines) that must follow a schema
    • Coding, data work and instruction-heavy prompts
    • Reading screenshots, charts and scanned figures

    The models inside ChatGPT

    GPT-5.6 (Sol / Terra / Luna)

    The newest generation. Sol is the flagship for the hardest reasoning, coding and agentic work; Terra is the balanced everyday model at meaningfully lower cost; Luna is the fast, cheap variant for short answers, classification and high-volume work. Start with Terra, escalate to Sol when an answer is wrong in an interesting way.

    GPT-5.5 and GPT-5.5 Pro

    The previous frontier tier. GPT-5.5 remains excellent for agentic coding and long professional documents; Pro spends more time reasoning per answer for genuinely hard problems, at a premium price and slower response.

    GPT-5.4 / 5.4 Pro / mini / nano

    The cost-efficient workhorse line. 5.4 is a strong frontier-adjacent model at a lower price; mini handles bulk drafting and sub-tasks well; nano is built for extraction, ranking and classification where latency and price matter more than nuance.

    GPT-5, 5 mini, 5 nano, GPT-5.2

    Earlier GPT-5 tiers, kept for continuity. Still solid all-rounders; choose them if you have prompts already tuned to their behaviour, otherwise the 5.4/5.6 equivalents are usually better per dollar.

    Gemini

    Google DeepMind · Speed, long context and native multimodality

    Gemini's differentiator is throughput and context: the Flash tiers answer very quickly at a low price, and the family natively handles text, images, audio and video rather than bolting vision on. For research workflows that push whole papers, transcripts or long threads through the model repeatedly, Gemini is usually the cheapest way to get good quality.

    Pros

    • Excellent quality-per-dollar, especially in the Flash and Flash-Lite tiers
    • Very large context windows, so less chunking and fewer lost details
    • Genuinely multimodal — images, audio and video are first-class inputs
    • Low latency makes it pleasant for back-and-forth editing

    Cons

    • Flash tiers can be shallower than a flagship on hard, multi-hop reasoning
    • Formatting drifts more often on very long structured outputs
    • Behaviour changes more noticeably between versions than rival lines
    • Preview-labelled models can shift under you while still in preview

    Best for

    • Very long documents, transcripts and multi-file context
    • Fast iterative drafting where you re-run the prompt many times
    • Audio, video and image inputs alongside text
    • High-volume tasks where per-token cost dominates

    The models inside Gemini

    Gemini 3.6 / 3.5 / 3 Flash

    The current fast tier, newest first. 3.6 Flash is the best default: near-Pro quality on most everyday reasoning, coding and summarisation at Flash speed. 3.5 and 3 Flash remain good, cheaper fallbacks.

    Gemini 3.1 Flash-Lite

    The cost-efficiency tier. Built for classification, extraction, tagging and summarising at scale — use it when you are running hundreds of calls, not when you need an argument constructed.

    Gemini 3.1 Pro

    The reasoning tier of the current generation. Slower and pricier than Flash, but the one to pick for hard analysis, long synthesis and tasks where Flash keeps missing a step.

    Gemini 2.5 Pro / Flash / Flash-Lite

    The previous generation, still widely used for very long-document work. 2.5 Pro handles heavy workflows; 2.5 Flash-Lite is the cheapest option in the whole picker for trivial tasks.

    Claude

    Anthropic · The best writer, and the most careful reader

    Claude is the model people reach for when the output is going to be read by a human. It writes cleaner, less templated prose than its rivals, follows nuanced editorial instructions closely, and is comparatively willing to say a source does not support a claim. It is also strong at long-document analysis and at agentic coding in the Sonnet and Opus tiers.

    Pros

    • Most natural prose of the major families; least "AI-sounding"
    • Strong at nuance, caveats and admitting uncertainty
    • Excellent long-context comprehension for document Q&A
    • Sonnet tier gives near-flagship quality at mid-tier price

    Cons

    • More likely to refuse or heavily caveat borderline requests
    • Opus tiers are expensive and slower for routine work
    • Can be verbose unless you explicitly cap length
    • Weaker than Gemini on cheap high-volume batch work

    Best for

    • Essays, reports, emails and anything where tone matters
    • Editing and critiquing your own writing without rewriting your voice
    • Careful reading of long PDFs, contracts and papers
    • Agentic coding and refactoring tasks

    The models inside Claude

    Claude Opus 5

    The flagship. Deepest analysis, best on long chains of reasoning and complex codebases. Use it for the hard 10% of your work, not the routine 90%.

    Claude Sonnet 5 and Sonnet 4.6 / 4.5

    The balanced tier and the sweet spot for most users: close to Opus quality on writing and coding at a fraction of the cost. Sonnet 5 is the current default; 4.6 and 4.5 remain dependable.

    Claude Fable 5

    Tuned toward writing, research and open-ended problem solving. Pick it when the deliverable is narrative prose rather than code or structured data.

    Claude Opus 4.8 / 4.7 / 4.6 / 4.5

    The previous Opus generation, in descending recency. All are strong reasoners; the newer the number, the better the instruction-following and code generation, and the higher the price.

    Claude Haiku 4.5

    The small, fast tier. Great for quick replies, short rewrites and simple extraction where you still want Claude's tone.

    DeepSeek

    DeepSeek · Frontier-adjacent reasoning at a fraction of the price

    DeepSeek's models are open-weight-derived and priced aggressively, and their reasoning line is genuinely competitive on maths, logic and code. If you are cost-sensitive, or you want to see an explicit step-by-step chain before an answer, DeepSeek is the highest-value option in the picker. It is also strong in Chinese.

    Pros

    • Dramatically cheaper than Western flagships for comparable reasoning
    • Reasoning models expose an explicit thinking trace
    • Strong bilingual Chinese/English performance
    • Good at code generation relative to its price tier

    Cons

    • English prose is more mechanical than Claude or GPT
    • Reasoning models can over-think simple prompts and run long
    • Weaker multimodal support than Gemini or GPT
    • Data-residency and compliance review matters for regulated work

    Best for

    • Maths, logic puzzles and algorithmic problems
    • Cost-sensitive bulk reasoning work
    • Chinese-language writing and analysis
    • Seeing the model's working, not just its conclusion

    The models inside DeepSeek

    DeepSeek V4 Pro / V4 Flash

    The current generation. V4 Pro targets complex reasoning, code, multimodal and long-document tasks; V4 Flash is the lightweight sibling for fast everyday answers.

    DeepSeek V3.2 and V3

    The previous general-purpose line. V3.2 is faster on long text while holding quality; V3 remains a solid, very cheap general chat and long-form writing model.

    DeepSeek R1 and Reasoner

    The reasoning specialists. Both trade latency for accuracy on hard problems and show step-by-step working — best for maths, proofs and debugging, worst for one-line factual questions.

    DeepSeek Chat

    The plain conversational endpoint: no extended thinking, fast, and the cheapest way to use DeepSeek for ordinary drafting.

    Perplexity

    Perplexity · Live web grounding with citations attached

    Perplexity's Sonar models are not general chat models with a search button — they run a real search pipeline and then answer from what they retrieved, returning source URLs with the answer. That makes them the right choice for anything that happened recently or that must be verifiable, and the wrong choice for creative writing or code.

    Pros

    • Answers are grounded in live retrieved pages, not training memory
    • Citations returned by default, so claims are checkable
    • Much lower hallucination rate on factual questions
    • Deep Research tier runs many queries and produces a real report

    Cons

    • Weaker at creative writing, long-form style and code than GPT or Claude
    • Answer quality inherits the quality of whatever the web surfaced
    • Deep Research runs are slow and comparatively expensive
    • Less useful when you have already pasted all the source material yourself

    Best for

    • Current events, prices, releases and "as of today" questions
    • Any claim you will have to cite or defend
    • Fast market, competitor and background scans
    • Checking whether something you were told is actually true

    The models inside Perplexity

    Sonar

    The fast baseline: one search pass, short cited answer. Ideal for quick factual lookups.

    Sonar Pro

    Multi-step search with substantially more citations and longer answers — the default for real research questions.

    Sonar Reasoning / Reasoning Pro

    Adds chain-of-thought on top of live retrieval, so the model can weigh conflicting sources. Reasoning Pro is the stronger of the two for genuinely hard, contested questions.

    Sonar Deep Research

    Runs an extended multi-query investigation and returns a structured, heavily cited report. Expect minutes, not seconds — use it once per topic, not per turn.

    Grok

    xAI · Real-time social and web context

    Grok's distinguishing asset is its access to real-time X and web content, which makes it unusually good at reading public sentiment, breaking stories and the shape of a discussion as it is happening. Its Fast tiers are cheap enough to use casually, and its tone is noticeably less hedged than the other families.

    Pros

    • Unmatched real-time access to X/social discussion
    • Fast tiers are very inexpensive for their capability
    • Direct, low-hedging tone that some users strongly prefer
    • Grok 4 is a capable general reasoner, not just a news model

    Cons

    • Social sources are noisy — sentiment is not evidence
    • Less consistent than GPT or Claude on rigorous, structured deliverables
    • Tone can be too informal for academic or client-facing output
    • Smaller ecosystem and less predictable behaviour across versions

    Best for

    • Breaking news and "what are people saying right now" questions
    • Social sentiment, narrative and trend reading
    • Blunt, direct answers without heavy caveats
    • Cheap, fast general chat via the Fast tiers

    The models inside Grok

    Grok 4

    The flagship: strong general reasoning combined with real-time X and web grounding. The one to use when a question is both hard and current.

    Grok 4 Fast (Reasoning) and Grok 4 Fast

    Same generation, cheaper and quicker. The Reasoning variant thinks before answering and is better on multi-step problems; the non-reasoning variant is the fastest option for everyday questions.

    Grok 3 and Grok 3 mini

    The previous generation. Grok 3 is a competent general chat and analysis model; mini is the lightweight tier for simple, high-volume tasks.

    How to choose in practice

    Match the tier to the difficulty, not to your budget anxiety. Flagships (GPT-5.6 Sol, Claude Opus 5, Gemini 3.1 Pro, Grok 4) cost several times more per answer than the mid tiers, but you rarely need more than a handful of flagship answers per project. Draft on a mid tier, escalate only where the mid tier visibly fails.

    Switch models mid-thread. Multi-Model Chat keeps the conversation when you change models, so a natural workflow is: research the facts with Perplexity Sonar Pro, structure the argument with GPT-5.6, then hand the draft to Claude Sonnet 5 to make it read like a human wrote it.

    Verify anything load-bearing. Every model on this list can state a wrong fact fluently. Grounded models (Perplexity, Grok) reduce this but do not eliminate it — citations can point at pages that do not support the claim. Check the sources behind the three or four claims your work actually depends on.

    Open Multi-Model Chat Browse all guides

    Model line-ups change frequently. This guide covers the 50 models currently available in Research Monkey's Multi-Model Chat and is updated when the picker changes.