Wholestory

At 2.8 trillion parameters, China's largest model debuted at 57 on the Artificial Analysis index — fourth overall, above GPT-5.5 and behind only Claude Fable 5 and GPT-5.6 Sol — and second on AA's agentic knowledge-work benchmark. Chinese AI rivals' shares plunged on the news (Z.ai -28%, MiniMax -16%). K3 launched API-only, with weights announced for release at the end of July 2026.

Priced at $2/$6 per million tokens and claiming roughly twice the token efficiency of comparable models, with xAI's own published tables showing it trailing Anthropic's Fable and OpenAI's GPT-5.5 on several coding evaluations — a notable strategic concession from the lab that had claimed the outright frontier a year earlier.

Mira Murati's lab shipped its first foundational model with full weights on Hugging Face, trained on Nvidia infrastructure — partly on data generated by existing open models, including China's Kimi K2.5. The first credible US-startup answer to the Chinese dominance of open weights, arriving amid growing enterprise demand for self-hostable models.

The flagship Sol — positioned for complex work across coding, research, science, and cybersecurity — began rolling out in ChatGPT under the trusted-partner limits announced in June, priced at $5/$30 per million tokens for short context. Independent scoring put Sol (max) at 59, second only to Claude Fable 5.

The institute's first public open-vs-closed analysis found GLM-5.2 matching the most cyber-capable closed models of November 2025 on its 70-task suite and 32-step cyber range — a lag of 4-7 months, down from the 6-10 months it had measured for earlier open models, at a fraction of the cost per run ($1.19 versus $85 for one 100M-token range run). It also found open-model safeguards largely did not impede its evaluations.

Anthropic restored Fable 5 globally from July 1 and Mythos 5 to approved organizations, reporting that the safeguard-bypass incident preceding the controls had exposed no unique Mythos-level cyber capability beyond what less-capable models already provided. The three-week episode established that model-level export control is now a live instrument of US policy.

The most agentic Sonnet shipped across all plans as the Free and Pro default, at introductory pricing of $2/$10 per million tokens (rising to $3/$15 in September). Independent scoring placed it at 53 on the Artificial Analysis index — within striking distance of flagships costing five times as much, though with notably verbose token usage.

Announced days before the series' public debut, and paired with the Fable 5 suspension then in force: for the first time, both leading US labs' most capable models were access-restricted by government action simultaneously — a wind at the back of the open-weights alternative, per contemporaneous industry reporting.

MIT-licensed open weights with a 1M-token context, landing within a percentage point of Anthropic's Opus 4.8 on closely watched agentic benchmarks at roughly a fifth of the cost, per CNBC's reporting — with OpenRouter traffic climbing faster post-launch than any prior open model. Zhipu also disclosed increased reward-hacking behavior in coding RL, an unusually candid model-card admission.

An unprecedented intervention: export controls on specific commercial AI models, requiring nationality-based restriction. Unable to verify nationality in-product, Anthropic suspended access rather than operate out of compliance — the first time a frontier model was pulled from the market by government order.

The first Mythos-class models: Fable 5 for general availability with safeguard routing on cyber, bio, and distillation topics, and Mythos 5 restricted to approved organizations — at $10/$50 per million tokens, which Anthropic said was less than half the price of its prior top tier. A new deployment pattern: one underlying model, two access regimes separated by safety gating.

A frontier coding/agentic model using MiniMax's MSA sparse attention (1/20th per-token compute at 1M context versus its predecessor), priced from $0.30/$1.20 per million tokens. Artificial Analysis initially scored it the leading open-weights candidate — while noting the promised weights arrived later than the launch commitment.

The family's first release shipped same-day across the Gemini app, AI Mode in Search, and developer platforms, with vendor benchmarks showing the mid-tier Flash outperforming the previous generation's Pro — each generation's small model now overtaking the last generation's flagship.

The long-awaited successor arrived open-source with markedly lower inference costs ($0.435/$0.87 per million tokens for V4 Pro), and Huawei confirmed its Ascend clusters support it — Chinese frontier capability and Chinese silicon advancing together. Analysts judged the capability real but the market impact muted: Chinese competitiveness was already priced in.

Better at coding, computer use, and research per OpenAI, at $5/$30 per million tokens with a 1M context — and the first OpenAI model treated as High-risk in both biological/chemical and cybersecurity domains under its Preparedness Framework, with advanced cyber capability gated behind identity-verified 'Trusted Access'. The UK AISI independently measured GPT-5.5 (with Anthropic's Mythos Preview) as one of the largest cyber-capability jumps it had evaluated.

The models that defined 2024-2025 left the consumer product in a single sweep (API access unchanged at the time), an unusually fast deprecation cycle that underlined how quickly frontier capability was being superseded — GPT-5 lasted just over six months as a ChatGPT model.

V3.2 and the reasoning-maxed V3.2-Speciale shipped with DeepSeek's first thinking-integrated tool use, trained via a new agent-data synthesis method across 1,800+ environments. The lab's claims — 'GPT-5 level' V3.2, Speciale 'rivals Gemini-3.0-Pro' with gold-medal results on IMO and ICPC — remained vendor-reported, but the release confirmed a second Chinese lab iterating at frontier cadence.

Six days after Gemini 3, Anthropic claimed the agentic-coding state of the art on SWE-bench Verified and cut Opus pricing 3x from $15/$75 to $5/$25 per million tokens — the frontier release cadence and its price curve now both compressing at once.

Gemini 3 Pro topped LMArena at 1501 Elo and scored 37.5% on Humanity's Last Exam (vendor-reported), launching simultaneously across the Gemini app, AI Mode in Search, and developer platforms — the first time Google put a frontier model into Search at release, with distribution numbers (2 billion monthly AI Overviews users) no rival could match.

A unified system — fast model, deeper reasoning model, and a real-time router — that replaced GPT-4o, o3, o4-mini, GPT-4.1, and GPT-4.5 as ChatGPT's default for its nearly 700 million weekly users, at $1.25/$10 per million tokens in the API. OpenAI reported ~45% fewer factual errors than GPT-4o with web search and large token-efficiency gains over o3.

gpt-oss-120b and gpt-oss-20b shipped under Apache 2.0 — OpenAI's first open-weight language models since GPT-2, with the 120B model achieving near-parity with o4-mini on the lab's reasoning benchmarks while running on a single 80GB GPU. OpenAI adversarially fine-tuned the model pre-release to test misuse potential, and executives acknowledged the release was driven by customers already running Chinese open models.

Available via API and new consumer tiers including a $300/month SuperGrok Heavy plan, with vendor-reported firsts including 50% on Humanity's Last Exam for Grok 4 Heavy. Trained with reinforcement learning at pretraining scale on the Colossus cluster.

Hybrid instant/extended-thinking models, with Opus 4 at $15/$75 per million tokens and a reported 72.5% on SWE-bench Verified. Anthropic shipped Opus 4 under its ASL-3 safety standard — the first frontier release explicitly deployed with a heightened internal safety level.

A family spanning 0.6B to 235B parameters with switchable thinking and non-thinking modes and a controllable thinking budget, trained on over 36 trillion tokens. Qwen was already, with DeepSeek, making Chinese releases the default substrate of the open ecosystem.

The new reasoning models could invoke every ChatGPT tool — web search, Python, visual reasoning, image generation — inside a chain of thought. OpenAI reported the o3 cost-performance frontier strictly improved on o1's, evidence that the reasoning axis was compounding rather than saturating.

Scout and Maverick shipped as open-weight multimodal MoE models, but Meta's headline LMArena Elo of 1417 came from an unreleased experimental chat configuration, not the published weights — drawing independent criticism, and diverging evaluations: Artificial Analysis rated the models among the best non-reasoning releases while other independent testers found them far weaker than claimed.

Released as a free experimental model, 2.5 Pro debuted at #1 on the LMArena human-preference leaderboard by what Google called a significant margin, with reasoning built in — the beginning of a sustained Google run at the top of crowd rankings that lasted more than six months.

Trained on xAI's 200,000-GPU Colossus cluster, Grok 3 launched consumer-first (no API at release) with reasoning modes and a claimed Chatbot Arena Elo of 1402 — the fastest any new lab had reached the frontier conversation since Anthropic.

R1 shipped under an MIT license with API pricing of $0.55 per million input tokens and $2.19 output — versus $15/$60 for the o1 it claimed parity with on math, code, and reasoning benchmarks — alongside six distilled smaller models. The release triggered a global market repricing of AI economics within a week and made a Chinese lab, for the first time, the reference point in the frontier conversation.

A 671B-parameter (37B active) mixture-of-experts model with open weights, trained — per DeepSeek's technical report — in just 2.788 million H800 GPU-hours on 14.8T tokens, with day-one inference support on AMD and Huawei Ascend hardware. The claimed training economics, on export-restricted hardware, previewed the disruption its successor would cause a month later.

o1-preview and o1-mini — trained, per OpenAI's research lead, with a new optimization approach that spends more compute thinking before responding — launched at $15/$60 per million tokens, roughly six times GPT-4o's price. Test-time compute became the industry's second scaling axis, and 'reasoning' variants became standard across every major lab within a year.

Trained on over 15 trillion tokens across more than 16,000 H100 GPUs, the 405B model was assessed by independent analysts as genuinely in the same class as the closed frontier — the first time open weights reached it. The license still restricted the largest platforms and required attribution, keeping the 'open source' label contested even as the capability gap closed.

OpenAI's small-model replacement for GPT-3.5 Turbo shipped at $0.15/$0.60 per million tokens — an order of magnitude cheaper than prior frontier models, and by OpenAI's own accounting a 99% cost drop per token versus its 2022-era text-davinci-003. The collapse in the price of a fixed level of capability was now explicit vendor strategy.

Anthropic's new mid-tier model kept Sonnet pricing ($3/$15 per million tokens) while beating Claude 3 Opus on the lab's evaluations — solving 64% of problems in an internal agentic coding evaluation versus Opus's 38%. Capability-per-dollar, not raw capability, was becoming the competitive front.

Apple's WWDC announcement paired a ~3 billion parameter on-device model (compressed to ~3.7 bits per weight, with swappable per-feature LoRA adapters) with a larger server model on Apple silicon. In Apple's own evaluations the on-device model beat comparable small open models — the clearest early evidence for a second, edge-scale track in the model race.

An end-to-end multimodal model ('o' for omni) responding to audio in near-human conversational time, priced at half of GPT-4 Turbo in the API and — for the first time for an OpenAI flagship — available to free ChatGPT users. The price of GPT-4-class capability was now falling on a steep curve.

Haiku, Sonnet, and Opus, priced at $0.25/$1.25, $3/$15, and $15/$75 per million tokens respectively, all with 200K context. Anthropic's published benchmark table showed Opus beating GPT-4 across common evaluations — the first credible claim on GPT-4's crown since its release a year earlier.

Announced with a standard 128K context and a 1-million-token window in private preview — the longest of any large-scale foundation model at the time — built on a mixture-of-experts architecture Google said was cheaper to train and serve. Context length became a new axis of frontier competition alongside raw capability.

A sparse mixture-of-experts model (46.7B total parameters, 12.9B active per token) under a genuinely permissive license — no research-only gate, no commercial restrictions. Mistral claimed it outperformed Llama 2 70B with 6x faster inference and matched GPT-3.5 on standard benchmarks, making it the strongest truly-open model of its moment and the European entry in the frontier race.

Three sizes — Ultra, Pro, and Nano — with Nano shipping on-device on the Pixel 8 Pro, the first frontier-lab model engineered into a phone. Google claimed Ultra exceeded state-of-the-art on 30 of 32 academic benchmarks and was the first model to beat human experts on MMLU at 90.0% — vendor figures that independent same-day coverage noted rested on very close margins.

Weights and starting code for pretrained and fine-tuned versions, free for research and commercial use, with Microsoft as preferred distribution partner via Azure. Meta reported over 100,000 researcher access requests for the first LLaMA. Open-weight frontier-chasing became a deliberate corporate strategy rather than a leak — though the license's restrictions meant 'open source' was contested from day one.

A large multimodal model accepting image and text input, released via ChatGPT Plus and an API waitlist at $30/$60 per million tokens (8K context; the 32K version cost $60/$120). OpenAI also open-sourced its evaluation framework, OpenAI Evals. GPT-4 became the reference point the rest of the industry measured against for the next year.

After a closed alpha with partners including Notion and Quora, Anthropic opened Claude via chat interface and API, in two tiers: Claude and the lighter, cheaper Claude Instant — putting a second US lab's frontier-adjacent model into the market the same week as GPT-4.

Roughly two weeks after Meta announced LLaMA as a research-access release requiring an application, the model weights appeared as a downloadable torrent on 4chan. Observers split between warning the technology would be misused and arguing open availability accelerates safety research — the first major access rupture of the frontier era, and an accidental preview of the open-weights ecosystem to come.

A conversational model fine-tuned with RLHF from a GPT-3.5-series base went live to the public at an effective price of $0. OpenAI's own launch notes disclosed the system 'sometimes writes plausible-sounding but incorrect or nonsensical answers.' The release turned large language models from a developer API into a mass consumer phenomenon and set off the competitive race that defines this page.

DeepMind published its compute-optimal training analysis: a 70B-parameter model trained on 1.3 trillion tokens outperformed far larger models like the 280B Gopher and 530B Megatron-Turing NLG. The finding — that the era's models were oversized for their compute budgets and undertrained on data — reset every lab's scaling recipe and foreshadowed the industry's turn toward smaller, cheaper-to-serve models.

OpenAI released its first commercial product: a private-beta 'text in, text out' API serving the GPT-3 family. It explicitly chose API access over open-sourcing the weights, arguing access can be revoked if misused — and that the models were so large and expensive to run that only big companies could otherwise afford to deploy them. The pattern set here — frontier capability behind a metered API — defined the market for years.