This blog is where I'll write down what I learn along the way. Not announcements, not predictions, just notes from actual work.

What I mean by "practical"

A lot of AI talk is about what models might do some day. I'm more interested in what they reliably do today, and in where they still fail. In legal and educational work, that difference matters. A tool that summarises a document well nine times out of ten still needs someone to check the tenth.

So the questions I keep coming back to are simple:

• Does this actually save someone time, or does it move the work somewhere else?
• Can a person see where an answer came from and check it?
• What happens to the data, and who controls it?
• What does it do when it doesn't know?

Legal AI

I have a background as a legal service provider, and I see how hard it can be for people to find clear information about their situation, especially in stressful cases like youth care. That's part of why I work on Steunvoor.nl, a free platform that gives parents clear information and support.

AI can help explain procedures, structure documents and point people to the right questions. It should not pretend to replace professional advice, and it should be explicit about its limits. Much of the design work is about exactly that: sources, uncertainty and knowing when to hand off to a human.

Educational AI

On the education side I'm building LeerHeld.nl, a learning platform for children and parents without ads or distractions. Here AI is useful for generating practice material and giving feedback, but children deserve tools that are calm, predictable and safe. I'd rather ship something modest that works than something impressive that confuses.

Agents and self-hosting

Behind the scenes I run a set of AI agents that each do one job: research, writing drafts, monitoring servers, keeping my projects in order. They run on my own servers and a small homelab, with cloud services where they make sense. Running things yourself teaches you quickly where the real costs and failure modes are: rate limits, flaky logins, memory that drifts, and agents that confidently do the wrong thing twice.

I'll write about those lessons too, including the mistakes.

What to expect here

• Write-ups of things I built and what went wrong
• Notes on agent design, open models and self-hosted infrastructure
• Thoughts on using AI carefully in legal and educational contexts
• A curated list of links and repositories I find genuinely useful

If something here is useful to you, or you think I'm wrong, I'd like to hear it. You can find me on LinkedIn, GitHub or via victorgrosser.nl.

This is the second half of my look back at 2026's model releases. The April–June recap was about cadence and agents. July to October was about price, gating and an almost absurd release frequency. As before, prices are list API prices per million input/output tokens at launch.

July: the busiest month for flagships

• Claude Sonnet 5 (1 July) became the default for Free and Pro users and closed much of the gap to Opus 4.8 on agentic work at a fraction of the cost.
• GPT-5.6 Sol, Terra and Luna (9 July). Three tiers at three prices: Sol $5/$30, Terra $2.50/$15 and Luna $1/$6. They launched together with GPT-Live, OpenAI's first full-duplex voice model.
• Grok 4.5 (9 July) went public within hours of GPT-5.6, with big claims but, as The AI Cook noted, no system card or benchmark table.
• Meta Muse Spark 1.1 (9 July) was Meta's first paid model ($1.25/$4.25), a clean break from its open-weights history.
• Gemini 3.6 Flash and Flash Cyber (22 July). A cheaper Flash plus a security-tuned twin.
• Claude Opus 5 (late July): close to Fable 5 intelligence at roughly half the price, with Opus pricing held at $5/$25.

July is where launches started to lead with the invoice. Three tiers from OpenAI, a cheaper Flash from Google, and an Opus that halved the cost of near-top capability all point the same way.

August: price cuts and open weights

No new top-tier flagship from the big three in August, so the action moved elsewhere:

• Gemini 3.7 Flash (14 August) at $0.75/$3.75, half the price of 3.6 Flash. DeepSeek V4-Pro went generally available the same day.
• Grok 4.6 (13 August) with frontier-level index scores at $2/$6.
• GPT-5.6-Cyber (11 August), gated behind vetted tiers, the same week Meta open-sourced a 30B agentic model, Muse Glimmer.
• A wave of open-weight and Asian-lab releases (Qwen3.8, GLM-5.3-Flash, a new DeepSeek V4 Flash checkpoint), covered in my open-weight models recap.

September: the densest month of the year

• Claude Fable 5.1 (2 September) with cache reads 75% cheaper, which The AI Cook estimates as about 25% off a typical workload.
• Gemini 3.8 Flash and 3.8 Flash Cyber (2–3 September). Google's own post describes it as building on 3.7 Flash from three weeks earlier, and its third Flash release in six weeks. Again the cyber variant was gated.
• GPT-6 Astra (4 September), built around computer use. OpenAI claimed 98% on FrontierMath Tier 4 and 99.9% on ARC-AGI-3. I'm repeating those as claims; I'd like to see independent replication before treating them as settled.
• Meta Muse (9 September), a personal agent on Muse Spark 1.3 that books, buys and fills in forms inside its own sandboxed virtual machine.
• DeepSeek-V4.1-Flash (10 September). DeepSeek's announcement describes a 552B-parameter MoE with 8B active parameters for input and 16B for output, a KV cache needing a quarter of the HBM of the previous generation, and results ahead of its own V4-Pro, which it began phasing out.
• Claude Opus 5.5 and GPT-6 Sol/Luna (23 September), on the same afternoon. Opus 5.5 moved Opus pricing to $4/$20; GPT-6 Sol and Luna halved the API price of the models they replaced.
• Claude Sonnet 5.5 (29 September) and GPT-6.1 Sol (30 September), the latter replacing GPT-6 Sol after a single week at $2/$10.

October so far

• Gemini 4 Argon (1 October), Google DeepMind's most powerful model, with a one-million-token output limit, released first to cyber defenders only.
• Kolibri (5 October), an Apache 2.0 German-English model from Aleph Alpha (78B, about 3.5B active) aimed at data that has to stay on your own servers. Interesting for European, on-premise use.
• GPT-5.5 retires on 14 October, less than six months after launch.

What I take from it

1. Release cycles are measured in weeks. GPT-6 Sol lived seven days. Don't chase every launch: keep your system model-agnostic and swap only when your own tests show a difference.
2. Read the pricing page, not just the benchmark. Caching discounts and tiered models delivered some of the largest real savings of the year.
3. The strongest models increasingly reach defenders first. Mythos 5, GPT-5.6-Cyber, the Flash Cyber models and Gemini 4 Argon all followed that route.
4. Transparency rules arrived mid-summer. Since 2 August, the EU AI Act requires disclosure when people interact with AI and machine-readable marks on generated content; see my EU AI Act recap.
5. Coding sells. Nearly every launch led with a coding or agentic score. That's where paying customers are, and it's worth asking whether those benchmarks match your own tasks.

Outside AI, the same months had their own milestones: Starship reached orbit and the Roman Space Telescope launched, covered in my space recap. For tools I actually use, see links & repos.

Sources

• The AI Cook: AI Model Release Timeline 2026 (updated 5 October 2026)
• Google: Introducing Gemini 3.8 Flash and 3.8 Flash Cyber (2 September 2026)
• DeepSeek: Introducing DeepSeek-V4.1-Flash (10 September 2026)
• OpenAI: Research releases

Spring 2026 was dense enough that it's easy to remember it as one blur of launch posts. I went back through release notes and trackers to separate what changed in practice from what was just noise. Prices below are list API prices per million input/output tokens at launch, as reported by the sources at the bottom.

April: four flagships in ten days

April is when the 2026 race properly started. Google, OpenAI, Anthropic and DeepSeek all shipped flagship models within about ten days of each other.

• GPT-5.4 on the free tier (10 April). OpenAI opened a reasoning-class model to free ChatGPT users. Not a new model, but it reset expectations of what "free" includes.
• Claude Opus 4.7 (mid-April). Anthropic shipped stronger coding and a higher image-input ceiling of roughly 3.75 megapixels, launched on five clouds at once, and kept pricing at $5/$25.
• Gemini 3 Pro preview, 3.1 Pro and 3 Flash (22 April). Announced at Google Cloud Next. Gemini 3 Pro took first place on LMArena at 1,501 Elo and scored 91.9% on GPQA Diamond, according to The AI Cook's tracker.
• ChatGPT Images 2.0 (22 April). Image generation that reasons before it draws, up to 2K resolution.
• GPT-5.5 (23 April). Pitched as a model for "real work": it plans, uses tools, checks itself and keeps going. It arrived six weeks after GPT-5.4 and reached ChatGPT, the API and GitHub Copilot within days.
• DeepSeek V4 preview (24 April). Open weights with a one-million-token context. V4-Pro has 1.6 trillion parameters (49B active), V4-Flash 284B (13B active). More on that side of the field in my open-weight models recap.

What stood out to me in April was not a single benchmark number but the cadence. A flagship that is six weeks old being replaced is a different world from the yearly cycles we had not long ago.

May: from chat to agents

May was a month of agents rather than models. The big labs spent it turning chatbots into things that act.

• GPT-5.5 Instant (6 May) quietly became ChatGPT's default for everyday chats.
• Grok Build (15 May). xAI's terminal coding agent, its answer to Claude Code and Codex, running up to eight workers, launched behind a $299 SuperGrok Heavy plan. On 29 May a Grok Build 0.1 model followed on the API at about 100 tokens per second and $1/$2.
• Gemini Spark and Gemini Omni (20 May). At Google I/O, Gemini was reframed as an always-on agent that can carry out tasks inside other apps, not just answer questions.
• Claude Opus 4.8 (28 May). SWE-bench Pro rose from 64.3% to 69.2% and a Fast mode ran about 2.5 times quicker, with no price increase.

The pattern here is that "the model" is becoming an implementation detail of a product that acts: a coding agent, an assistant inside other apps. For people building their own agents and MCP tools, that's encouraging and a warning at once: the interesting work moves to orchestration, permissions and evaluation.

June: gated capability and short lifetimes

• Microsoft MAI-Thinking-1 (3 June). Microsoft's first in-house reasoning model, shown at Build alongside MAI-Image-2.5. A clear sign it wants options beyond OpenAI.
• Gemma 4 QAT checkpoints (8 June). Quantisation-aware versions of Google's open Gemma 4 family; the smallest text model runs in under 1 GB of memory.
• Claude Fable 5 and Claude Mythos 5 (10 June). Fable 5 became the first Mythos-class model open to the public, at $10/$50. The unrestricted Mythos 5 went only to vetted cyber defenders.
• Retirements (mid-June). Anthropic retired Claude Sonnet 4 and Opus 4 on 16 June; OpenAI had retired GPT-5.2 days earlier.

The Fable/Mythos split is the June story I keep coming back to. A public model plus a restricted security variant turned into a pattern for the rest of the year: Google's Flash Cyber models, GPT-5.6-Cyber and Gemini 4 Argon all followed the same route. I think that's a reasonable response to dual-use capability, but it also means the "best model" you can benchmark is no longer the best model that exists.

What I take from these three months

1. Benchmarks moved, but the real change was the work around them. Models that plan, use tools and check themselves matter more than a few points on a leaderboard.
2. Plan for model churn. If a model can be retired weeks after its successor ships, hard-coding model names into a product is technical debt. Keep a small abstraction layer and your own evaluation set.
3. Price per task beats price per token. Faster modes, caching and smaller tiers often change the bill more than a headline price change.
4. Regulation runs in parallel. The same weeks saw the EU's Digital Omnibus deal on the AI Act; I wrote about that in The EU AI Act, April–October 2026.

A note on sources: much of the timeline below comes from third-party trackers that compile launch posts. I've linked first-party pages where I could; where only a tracker is cited, treat exact dates as "reported" rather than official.

Continue with AI models in July–October 2026, or browse my curated links & repos.

Sources

• The AI Cook: AI Model Release Timeline 2026 (updated 5 October 2026)
• BenchLM: AI model releases in April 2026
• OpenAI: Research releases
• DeepSeek: DeepSeek-V4.1-Flash announcement (with links to the V4 preview)