Editorial note (May 2026): This article was first published in December 2025 covering GPT 5.1, Claude 4.5 Sonnet, Gemini 3 Pro, and DeepSeek V3.2. All four models have since been superseded. We've rewritten the body for the current frontier (Claude Opus 4.7, GPT-5.2, Gemini 3.1 Pro, DeepSeek V4) and kept the original URL so existing links and citations still resolve. For the 2025 comparison as it stood, see our earlier four-way breakdown of Claude 4 vs ChatGPT o3 vs Grok 3 vs Gemini 2.5 Pro. For the Grok-inclusive view, see our Grok 4 vs Gemini 2.5 Pro vs Claude 4 vs ChatGPT o3 comparison.

If you only have ninety seconds, here's the call as of May 14, 2026:

• Hard coding and long-running professional work: start with Claude Opus 4.7. It's expensive ($5 input / $25 output per million tokens) but Anthropic's release is explicitly aimed at multi-step engineering and stronger instruction following, and it's what powers Cursor, Claude Code, and Windsurf at the high end.

• Default OpenAI workflows: GPT-5.2 is the current officially listed model on OpenAI's pricing page, at $1.75 input / $14 output. Some publishers reference GPT-5.5; OpenAI has not yet listed it on the public pricing page, so we use 5.2 here as the verifiable benchmark.

• Research, multimodal, long-context analysis: Gemini 3.1 Pro leads on Google's own model card scores (94.3% GPQA Diamond, 80.6% SWE-Bench Verified) and has the largest input context window at 1M tokens, with $2/$12 pricing under 200K and $4/$18 above.

• Cost-sensitive volume, agent worker calls, or anything you can self-host: DeepSeek V4 Flash at $0.14/$0.28 is roughly fifty times cheaper than the proprietary flagships on output tokens. V4 Pro is on a temporary 75% discount through May 31, 2026 if you want to test the heavier model.

Now the long version — and the part most comparison pieces skip: which one you actually want as a marketing operator, not as a developer.

What changed since December 2025

The four models that defined late 2025 — GPT 5.1, Claude 4.5 Sonnet, Gemini 3 Pro, DeepSeek V3.2 — are all retired or superseded. Between February and April 2026 every lab shipped a flagship update, and the practical answer for "which model should I use" shifted with each one.

The bigger structural change is that cost has eclipsed capability as the differentiating axis. Through most of 2024 and 2025, comparisons led with benchmark scores. In 2026 the spread on the headline benchmarks (GPQA Diamond, SWE-Bench Verified) has compressed to within a couple of points between the top six models, while the per-token pricing gap has widened to nearly two orders of magnitude. That changes how you should think about model selection — more on this below.

The other change worth naming: every model in this list is now meaningfully agentic. The argument has moved from "can it write code" to "can it autonomously execute a multi-step workflow without losing context." For most marketers reading this, the agentic question matters more than the raw-IQ question.

Pricing snapshot (the headline story of 2026)

Source: Anthropic, OpenAI, Google AI, and DeepSeek public pricing pages, checked May 10–14, 2026.

A workload that costs $50 a day on Claude Opus 4.7 costs roughly $2.80 on Gemini 3.1 Pro, $1.40 on GPT-5.2, or $0.56 on DeepSeek V4 Flash. At scale that's the difference between an experiment and a line item, and it's why most teams shipping AI in production have stopped picking one model and started routing across two or three. The "which AI is best" question is now mostly a question about which one is best where, and which one is cheap enough to handle the rest.

Best for coding

The coding benchmarks have compressed into a tight cluster at the top. Six frontier models now sit within roughly two points on SWE-Bench Verified, which used to be the headline differentiator. The interesting work has moved to the harder benchmarks — SWE-Bench Pro (multi-language, standardized scaffold) and Terminal-Bench 2.0 — where the spread is wider.

What that means in practice:

Claude Opus 4.7 is the safest first test for difficult coding, large refactors, and anything that has to hold its place across hours of work. It scored 64.3% on SWE-Bench Pro per Anthropic's announcement and leads computer-use benchmarks (78.0% on OSWorld). The Cursor and Claude Code communities consistently flag it as the model with the strongest intent-understanding on vague prompts — the kind of work where you say "fix this thing" and the model has to figure out what "this thing" is. The tradeoff is cost: at $25 output, you feel it on long sessions.

GPT-5.2 is the value-balanced choice for most software work and the right default if your stack is already inside the OpenAI ecosystem (Codex, ChatGPT for Teams, the OpenAI agent SDK). It's roughly seven times cheaper than Opus 4.7 on output and within a few points on most coding benchmarks. The terminal-execution and computer-use story is strong; it's the model most likely to run your code, see the failure, and iterate.

Gemini 3.1 Pro is the price-performance leader at the frontier. Google's model card lists 80.6% on SWE-Bench Verified and 68.5% on Terminal-Bench 2.0 — basically tied with Opus on the easier benchmark, slightly behind on the harder one, at 40% of the cost. The 1M context window matters when you're working in a large codebase and want the model to actually see all of it.

DeepSeek V4 Pro / Flash sits 4-8 points behind the proprietary flagships on coding benchmarks and 30-50x cheaper. For high-volume batch work — code refactoring across thousands of files, documentation generation, automated test writing — that math wins. For production code in a regulated context, the Chinese-server data path and the inconsistent API uptime are dealbreakers worth naming honestly.

Sequence to test: if you can afford it, start with Opus 4.7 on your hardest task, GPT-5.2 on your medium task, and DeepSeek V4 Flash on your high-volume task. That's the standard production stack for most teams shipping AI features right now.

Best for reasoning and research

The reasoning benchmarks tell a different story than coding. Here the spread is still meaningful and the leader is clearer.

Gemini 3.1 Pro leads on the two benchmarks that matter most for research workflows: 94.3% on GPQA Diamond (graduate-level science reasoning) and strong performance on Humanity's Last Exam, the hardest reasoning test publicly maintained (lastexam.ai). For analyst work, scientific synthesis, or anything that benefits from a 1M-token window holding multiple long documents simultaneously, this is the right starting point.

Claude Opus 4.7 is competitive on reasoning and pulls ahead specifically on tool-use and long-horizon planning. Anthropic's release explicitly emphasizes professional and analytical work, and the 1M-token context window (up from 200K in Opus 4.6) closes the gap on Gemini's headline advantage. Where Opus tends to win in practice is on tasks that require working memory across many steps — you're not just asking it to recall facts, you're asking it to hold a hypothesis while running tools to test it.

GPT-5.2 is the steadiest choice for general reasoning when you don't have a single benchmark to optimize for. Adaptive reasoning (the model decides how much thinking-time to spend) means simple questions stay cheap and hard ones get the compute they need, which matters when you're running heterogeneous workloads through one model.

DeepSeek V4 is surprisingly strong on competition math but uneven on agentic reasoning. For self-contained reasoning tasks — solve this proof, walk through this proof, derive this — it competes with the top proprietary models at a fraction of the cost. For browser-using, tool-chaining research workflows, it's not the right tool.

A note on benchmarks themselves: the AI labs and the leaderboards have been increasingly open about the fact that SWE-Bench Verified shows signs of training data contamination, and that MMLU is functionally saturated above 90%. We'd suggest weighting GPQA Diamond, ARC-AGI-2, and Humanity's Last Exam more heavily than the older benchmarks when you're picking a model — they resist contamination better and discriminate more between current frontier systems.

Best for writing

For most marketing teams this is the section that actually matters, because writing is where AI gets deployed before anything else gets deployed.

Claude Opus 4.7 produces the most natural long-form prose of any model in this comparison. The character holds across long outputs, the rhythm reads less mechanical, and the model is more willing to commit to a stance instead of hedging into mush. For thought leadership, narrative case studies, long-form essays, or anything where you'd rather have one strong piece than ten generic ones, Opus is the right tool. The Anthropic Sonnet 4.6 tier is also strong and is the cost-effective default if Opus pricing doesn't fit.

GPT-5.2 is the broadest writer and the easiest to wrangle into a specific tone. The eight personality presets (introduced with GPT-5.1, still available in 5.2) make voice consistency more tractable than with Claude, where you have to specify it in the prompt every time. For high-volume content production — product descriptions, ad variations, email sequences, SEO meta — GPT-5.2's combination of speed, cost, and instruction-following is the practical winner.

Gemini 3.1 Pro writes competently and precisely but less distinctively. Its prose tends toward the neutral, well-organized, slightly bloodless register that ranks reliably but rarely shines. If your brand voice is plain-English and practical, this is a feature; if you're trying to sound like a person, it's a constraint.

DeepSeek V4 produces better-than-expected writing for an open-weight model but has its own register. It tends conservative on imagery, more literal on instructions, and needs more voice coaching than Claude or GPT to avoid generic phrasing. For internal content or first drafts that a human will edit, it's fine. For final publication, the cost savings rarely justify the editing overhead.

A real tradeoff to name: Claude's writing strength comes partly from its refusal to default to listicle structures. If your CMS workflow wants bullet-heavy outputs, Claude will fight you. GPT-5.2 will give you exactly the bullets you asked for. Pick the model that matches your downstream format, not just the one that scores highest on prose quality in the abstract.

Best for AI search citation behaviour (the section nobody else writes)

This is the cut every other comparison article misses, and it's the one that matters most if you're a marketing operator running AI search and SEO services or trying to win generative engine optimization for your brand.

The question isn't only "which model is smartest." It's "which model's writing style is most likely to get cited by ChatGPT Search, Perplexity, Google AI Overviews, and the rest of the AI search surfaces your customers now use to find brands."

Three things we've observed running our AI visibility tracking platform across hundreds of content programs:

First, citation behaviour is not the same as content quality. A prose-heavy Claude piece that reads beautifully often gets passed over for a more skimmable GPT piece with clear FAQ sections, structured headings, and the answer in the first sentence. AI search engines parse content the way Google's featured snippet algorithm parses it — they want the answer extracted cleanly, not earned at the end of a paragraph. This is consistent with what we found in our research on what AI search rewards in content.

Second, schema and structure matter more than voice. Claude writes the most distinctive prose, but distinctive prose is harder to extract. GPT-5.2's tendency toward explicit structure (headers, lists, "the answer is X") is a citation feature, not a bug, when the goal is to be quoted by AI. Gemini 3.1 Pro lands somewhere in the middle and benefits from its native integration with Google's surfaces.

Third, the measurement question is itself broken. Most AI visibility tools score citation rates without controlling for which AI generated the underlying content. If the same model is writing your content and indexing your content, the loop is closed in ways that affect both signal and noise. We've written about this measurement problem in detail in why AI search analytics breaks when AI is on both sides of the measurement.

The practical takeaway: if you're optimizing for AI search citation, write your top-of-funnel content with GPT-5.2 or Gemini 3.1 Pro for the skimmable structure, then use Claude Opus 4.7 for the long-form pieces that need to stand alone as substantive answers. Don't standardize on one model for content production. Route by content type.

Multimodal and long context

Long context first, because it's the most-tested claim in current model marketing and the most overhyped one.

The thing most comparison articles don't tell you: stated context window and retrieval quality are not the same thing. A model with a 1M-token window that retrieves accurately at 200K tokens is functionally a 200K-token model for most workloads. The "needle in haystack" tests that the labs publish are imperfect proxies. Test on your own documents before you commit.

For multimodal, Gemini 3.1 Pro is the clear leader on image, video, and audio understanding, and is the only model in this comparison built natively multimodal from the start. Claude Opus 4.7 added high-resolution vision (2,576px) in this release and is now competitive on image tasks. GPT-5.2 is strong on image understanding and has the broadest production multimodal ecosystem (transcription, voice, image generation, video understanding). DeepSeek V4 is text-only at the V4 Pro / Flash tier; separate models in the DeepSeek lineup handle vision.

The verdict for marketing operators

Most comparison articles end with the standard "no single model wins, use the right one for the task" closer. That's true but useless. Here's what we actually recommend, based on what we see working across the brands we run SEO and GEO services for:

If you're building a content production stack for a DTC brand or ecommerce SaaS: Default daily driver is GPT-5.2 (cost, speed, structure-friendly output, broad ecosystem). Escalation tier for hero content, thought leadership, and long-form is Claude Opus 4.7 or Sonnet 4.6. Cheap worker tier for high-volume product descriptions and ad variations is DeepSeek V4 Flash. Multimodal work (image analysis, video repurposing) routes to Gemini 3.1 Pro. See our AI search playbook for DTC beauty brands for what this looks like in production.

If you're building it for B2B SaaS: Default is GPT-5.2 with stronger weighting toward Claude Opus 4.7 for analyst-style content (whitepapers, vertical reports, sales enablement). Gemini 3.1 Pro for research synthesis when you need to ingest 50+ pages of source material in one prompt. DeepSeek mostly stays in agentic backend roles. We've documented the SaaS-specific configuration in our SaaS AI search and SEO playbook.

If you're running competitive AEO or platform optimization work specifically: The right answer is multi-model routing with explicit attribution tracking. Pick the model per content type, then track which AI surfaces cite which content. Most teams underinvest in the measurement side and end up shipping content for AI search without knowing what's working. Our revenue-by-keyword attribution and content refresh analytics work is built exactly for this gap.

The picks-vs-cost math is also worth running explicitly. Most teams overspend on Opus or 5.2 for tasks that DeepSeek would handle adequately, and underspend on Opus where it would genuinely move conversion. Our breakdown of what GEO and AI SEO services actually cost in 2026 walks through the real numbers.

What about Grok 4.20?

Worth naming since the absence will be obvious to anyone coming from a five-way comparison: xAI's Grok 4.20 sits at or near the top of LMArena and leads on a few benchmarks (Mensa Norway visual reasoning, real-time information access). We've covered Grok separately in our Grok 4 vs Gemini 2.5 Pro vs Claude 4 vs ChatGPT comparison, and the short version is: Grok matters most for use cases that need live X / Twitter data or real-time signals, and matters less for content production. We held this comparison to four models to keep the focus on the daily-driver decision; Grok is a routing edge case rather than a default.

How we sourced this

Every benchmark number cited above traces to a primary source. Pricing numbers come from each provider's official pricing page, checked between May 10 and May 14, 2026. Specifically:

• Anthropic's Claude Opus 4.7 announcement and model page

• OpenAI's public pricing page

• Google DeepMind's Gemini 3.1 Pro model card and Gemini API pricing

• DeepSeek's V4 release notes and model pricing

• The original GPQA Diamond paper, SWE-bench paper and leaderboard, and Chatbot Arena for benchmark methodology

Benchmark scores reported by each lab are still provider-reported. We've named which source we used and avoided cross-comparing numbers from different evaluation harnesses, because the underlying agent scaffold often matters more than the model weights. Where we've cited a leadership claim ("Anthropic positions Opus 4.7 as…"), we've used the provider's language and noted it as a claim rather than treating it as verified.

We'll refresh this article quarterly. If you're reading this more than ninety days after the "Last verified" date at the top, treat the specific scores as approximate and run your own evaluation before committing API spend.

Should you switch?

If you're already on GPT-5.1 for production content work, the upgrade to GPT-5.2 is a no-brainer — same price tier, better instruction-following, official OpenAI listing. If you're on Claude 4.5 Sonnet, moving to Sonnet 4.6 is cleaner than the jump to Opus 4.7 unless your tasks are genuinely Opus-tier. If you're on Gemini 3 Pro, 3.1 Pro is a meaningful upgrade and worth running a side-by-side evaluation on. If you're on DeepSeek V3.2, V4 Flash is dramatically cheaper for the same workload class and worth the migration.

The bigger question isn't "which one model" but "which routing setup." Single-model standardization makes sense in two scenarios: small teams where the operational overhead of multi-model routing outweighs the cost savings, and enterprises with strict data sovereignty constraints that limit which providers can touch their data. Everywhere else, the right answer in 2026 is a stack — and the right partner is one that can tell you which model to use on which content type, then prove the choice is working with real attribution data.

That's what we do. If you're trying to figure out the right AI model routing strategy for your content program, book a call or look at how our managed service is priced. We compare our approach against alternatives like Otterly, Omniscient Digital, and others in our side-by-side comparison pages if you're at the diligence stage.

Last verified: May 14, 2026. Next scheduled refresh: August 2026 or sooner if a major lab ships a new flagship.