Best Claude Code Mods and Where to Find Them
The best Claude Code mods by job: usage-meter for plan limits, context-bar for context, whats-agent-doing for live activity, and how to vet the rest.
Read the analysisFollow the money behind intelligence.
AI looks like software. Underneath, it is a system of chips, power, models, labor and capital. Choose a thread and follow it all the way down.
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Today on the desk.
Four readings, each dated and linked to the tracker it came from.
New AI models
24shipped in October 2026
17 labs. Newest: Gemini 4 Carbon from Google.
+11 moreAs of Oct 29, 2026
32GB DDR5 kit at retail
$429 to $600$13.41 to $18.75 per GB
Plus DDR4, HBM and server RDIMM street prices.
As of Oct 27, 2026
Model prices and lifecycle
54models with verified rates
2 more announced or rumored. Every rate is source-linked.
As of Oct 29, 2026
AI benchmarks
120benchmarks, 12 categories
89 still active, the rest saturated or retired.
As of Oct 20, 2026
More from the desk.
All 221 analyses
GPT-6.1 Terra: Pricing, Benchmarks, Cost per Task
GPT-6.1 Terra costs $1/$5 per million tokens, half of GPT-6.1 Sol. It scores 48 on Artificial Analysis for $0.40 a task, 56% of Sol's cost per task.
Anthropic IPO: How Does Anthropic Make Money?
Anthropic's IPO filing: 80% of its $16.3B first-half revenue came from metered tokens, 48% via cloud marketplaces, and Q2 2026 was its first operating profit.
Microsoft Layoffs 2026: Why Is Microsoft Cutting Jobs?
Microsoft offered buyouts to 60,000 staff after spending $115.9B on equipment in fiscal 2026, 2.4 times what its 223,000 employees cost at median pay.
Why Are OpenAI and Anthropic Cutting AI Prices?
OpenAI and Anthropic cut API prices 20% to 92% from July to October 2026. Open-weight models hosted by US clouds set the floor; cheaper serving did the rest.
Can AI Agents Communicate Through a CPU Cache?
Yes. OpenAI agents in separate VMs passed secrets by timing CPU cache reloads, but they were asked to. The one they built unprompted used a package cache.
Do AI Games Sell on Steam? What Next Fest 2026 Shows
About 35% of October 2026 Steam Next Fest demos disclose AI, but only 2 of the top 25 do. A controlled study puts the review penalty near 53%.
Why Is Claude Blocking Reverse Engineering?
Claude Opus 5.5 is cutting off REA reverse-engineering sessions mid-reply. Why the cyber classifier fires, what still works, and how to get access back.
Kimi K3.1 Pro: Pricing, Benchmarks, Open Weights
Kimi K3.1 Pro costs $2 and $10 per million tokens, a third below Kimi K3, and scores 55 on the AA index for $2.11 a task, 61% less than Claude Sonnet 5.5.
What the numbers look like.
Three pictures from the same datasets the trackers run on. Each one is drawn at build time from a dated snapshot, not a live feed.
Intelligence you get per dollar you spend.
| Model | Intelligence Index | Blended price per Mtok | On the cost-efficiency frontier |
|---|---|---|---|
| Gemini 4 Carbon | 59 | $4.00 | Yes |
| Claude Opus 5.5 | 58 | $8.00 | No |
| GPT-6 Astra | 57 | $20.00 | No |
| GPT-6.1 Sol | 55 | $4.00 | No |
| GPT-6.1 Terra | 51 | $2.00 | Yes |
| Gemini 4 Argon | 50 | $4.00 | No |
| Kimi K3.1 Pro | 46 | $4.00 | No |
| Muse Wonder | 43 | $8.00 | No |
| Claude Haiku 5.5 | 36 | $0.20 | Yes |
| Grok 4.7 | 35 | $3.00 | No |
| Qwen3.8 Max | 34 | $3.00 | No |
| DeepSeek V4.1 Flash | 32 | $0.52 | No |
| GLM-5.3 | 32 | $2.20 | No |
| MiniMax-M3.1-Flash | 29 | $0.26 | No |
| GPT-6 Luna | 28 | $0.20 | No |
| Mistral Large 4 | 25 | $1.03 | No |
| Muse Spark Open | 25 | $2.00 | No |
| Inkling | 12 | $2.57 | No |
| Nemotron 3 Ultra | 12 | $1.40 | No |
A 32GB DDR5 kit, since April 2025.
| Date | Typical listing | Cheapest tracked |
|---|---|---|
| 2025-04-04 | $125 | $59 |
| 2025-06-15 | $128 | $50 |
| 2025-08-08 | $132 | $55 |
| 2025-10-01 | $138 | $53 |
| 2025-11-24 | $344 | $124 |
| 2026-01-17 | $529 | $150 |
| 2026-03-12 | $553 | $150 |
| 2026-05-05 | $550 | $150 |
| 2026-06-28 | $566 | $375 |
| 2026-08-21 | $578 | $390 |
| 2026-10-07 | $597 | $396 |
| 2026-10-10 | $605 | $396 |
| 2026-10-13 | $600 | $396 |
| 2026-10-16 | $607 | $429 |
| 2026-10-19 | $609 | $429 |
| 2026-10-22 | $609 | $429 |
| 2026-10-25 | $606 | $429 |
| 2026-10-26 | $607 | $429 |
The price of intelligence, 150x down.
| Date | Cheapest GPT-4-class model | Top-tier flagship |
|---|---|---|
| 2021-11-01 | $60 | |
| 2023-03-14 | $60 | $60 |
| 2024-03-04 | $75 | |
| 2024-07-18 | $0.60 | |
| 2025-02-08 | $0.60 | |
| 2025-07-14 | $75 | |
| 2025-11-24 | $25 | |
| 2026-06-09 | $50 | |
| 2026-07-01 | $50 | |
| 2026-09-03 | $50 |
What AI really costs.
Every number checked, dated, and linked to its source.
The price you are shown is never the price you pay. Your flat monthly plan is still metering tokens behind the scenes. Your coding agent burns most of its budget on tasks it never finishes. The data center sold in gigawatts arrives three years late, stuck in line for electricity.
This site works out the real figure and shows you where it came from.
Six subjects, one question: what does this actually cost to run?
- AI costs
- What one task costs start to finish, not the sticker price per token.
- Coding agents
- Why the setup around a model drives your bill more than the model does.
- Models and benchmarks
- Whether a leaderboard score holds up on real work. Usually it does not.
- Local AI and hardware
- Buy the GPU or rent the API? The math, redone every time RAM prices move.
- Compute infrastructure
- The power, the buildings, and the debt behind every model you use.
- AI markets
- Who is making real money from AI, and who is only moving it around.
The free trackers handle the moving parts. Model prices, GPU and memory street prices, benchmark definitions, provider rates, cost per task: each one is a dated dataset, read again at the source whenever the market shifts. You will never be handed a screenshot from six months ago.
Nothing on this site is guessed. A benchmark score comes off the leaderboard, not out of a launch post. A price links to the page it was read from. No ranking is for sale, and any paid link is labelled as one. Anything that turns out to be wrong gets corrected in the open.
Check the numbers yourself.
Every figure links to its source right where it appears, not in a footnote at the bottom. The data behind the trackers is published as JSON, so you can cite it directly instead of scraping the page. Each post says how many sources it stands on and when those numbers were last checked.
The label matters as much as the link. A preprint is called a preprint. Documentation written by a vendor is called documentation, not independent proof. Coverage from another outlet says whose reporting it is.
Prices and scores are stamped with the date they were confirmed. A rate checked in March proves nothing in August, and leaderboards reshuffle faster than any page can keep up. When a number is estimated rather than measured, the assumptions are laid out so you can redo the math, or argue with it.
- Published analyses
- 221
- Free tools
- 33
- Open datasets
- 22
- JSON endpoints
- 9
- Last published
- Oct 31, 2026
Questions about this site
- What does Capital & Compute cover?
- The economics of AI infrastructure: what coding agents and model APIs cost per finished task, whether running inference locally beats renting it, what a benchmark score means once you leave the test set, and the capital, power and memory supply sitting behind the models. Six topic areas, 221 published analyses, and a set of maintained price and benchmark trackers.
- How are the numbers sourced?
- Every factual or statistical claim links to its primary source at the point the claim is made, names who produced it, and states the year. A preprint is labeled a preprint. Vendor documentation is labeled documentation rather than an independent finding. Posts built on research close with a full source list, and each figure records the date it was checked.
- How often are the price trackers updated?
- Each tracker carries an as-of date and is refreshed against primary sources when the underlying market moves, not on a fixed schedule. Model pricing follows provider rate cards; memory, GPU and storage prices follow spot indices, contract forecasts and retail listings. A reading is a dated snapshot rather than a live feed, so confirm anything time-critical at the source before quoting it.
- Can I reuse the data?
- Yes. The datasets behind the trackers are served as JSON at stable URLs so a number can be cited or rebuilt instead of scraped, and an llms.txt describes the site for AI answer engines. Attribution with a link back is appreciated.
- Who writes it?
- Capital & Compute publishes under the brand rather than a personal byline. The work is meant to be judged on whether its sources hold up, which is why every claim is linked and dated, why methodology is stated alongside the number, and why corrections are published rather than edited in quietly.