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Zai has released GLM-5.2
extends the context window from 200K to 1M tokens and adds High/Max thinking-effort controls
scores 81.0% on Terminal-Bench 2.1 and 62.1% on SWE-bench Pro
Issue 24 is out, covering agentic coding updates from last week:
Claude Fable 5 & Mythos 5 access pulled by US government, Kimi K2.7 Code, GLM-5.2, MiMo V2.5 Pro UltraSpeed, and MiMo code
Read it in your inbox!
Fable 5 is on the API and consumption-based Enterprise plans now, and included on Pro, Max, Team, and seat-based Enterprise plans only through June 22
After that it requires usage credits until capacity allows it to return as a standard plan feature.
Anthropic has released Claude Fable 5, a new "Mythos-class" model that sits above the Opus tier
On agentic coding it scores 80.3% on SWE-bench Pro, ahead of the Mythos Preview model (77.8%), Opus 4.8 (69.2%), GPT-5.5 (58.6%), and Gemini 3.1 Pro (54.2%)
Issue 23 is out, covering agentic coding updates from last week:
MiniMax M3, Nemotron 3 Ultra, Qwen 3.7 Plus, Open Code Review, GitHub Copilot Desktop, and Uber's $1,500/month AI limit
Read it in your inbox!
DeepSWE Benchmark: Let's look into what it is first and then where it falls short
This is a benchmark for:
- longer-horizon tasks compared to SWE-Bench Pro
- tasks have a higher diversity and are contamination-free
This Month in Agentic Coding: May 2026
1. Tokens are getting both more expensive and cheaper.
The frontier proprietary models got more expensive. Gemini 3.5 Flash is 3x its predecessor 🧵
Launching paid tier for my agentic coding newsletter ($120/year)
You'll get a monthly email on the 1st of every month distilling the previous month of agentic coding updates into a single email
Read the first issue: agenticcodingweekly.com/p/acw-monthly-…
API pricing is tiered by input length:
- at ≤512K input tokens it runs $0.60/M input, $2.40/M output, and $0.12/M cached reads
- currently discounted 50% for 7 days to $0.30/$1.20/$0.06
- above 512K it doubles to $1.20/$4.80/$0.24
MiniMax has released M3 with 1M context window
Headline architectural change: MSA (MiniMax Sparse Attention)
Cuts per-token compute at 1M context to 1/20 of the previous gen, yielding 9× faster prefill and 15× faster decode
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