Waddling through AI so you don’t have to.
We test emerging AI models, agent architectures, and developer tools to see what they are actually good for, where they break, and whether vendor claims hold up. Reproducible benchmarks and field notes from running real workloads.
Models, Agents, and Tools We Cover
What each product is, what it costs, and where the vendor’s own docs disagree, checked against current first-party sources.
Meta
- Muse Spark: Meta’s hosted model family
- Muse Code: terminal coding agent
- Meta Muse: personal AI agent
xAI
- Grok Bot: AI teammates on a shared cloud computer
- Grok Build: terminal coding agent
OpenCode
- OpenCode: the open-source coding agent
- OpenCode Go: model subscription for coding agents
Anthropic
- Claude Opus 5.5: pricing, context, and benchmarks
- Gemini 4 Argon: access status, pricing, and benchmark reality
- Gemini 3.8 Flash: pricing, specs, thinking levels, and benchmarks
- Gemini 3.1 Pro: preview status, API IDs, pricing tiers, and benchmarks
- Google AX: Kubernetes control plane for sandboxed agent tasks
TypeSafe Jev: Empirical Benchmark & Architecture
TypeSafe claims Jev is a sub-second “System 1” decision model that replaces LLMs for classification and routing without text-generation overhead. We ran 11,000+ benchmark calls to see if those claims hold up.
What is TypeSafe Jev?
How Jev evaluates state in a single parallel pass, returns typed decisions (choice, score, and noul) with zero output fees, and where its sharp boundaries lie (no text generation, arithmetic, or calendar logic).
- Parallel evaluation: Evaluates typed questions simultaneously across input state instead of streaming tokens autoregressively.
- Zero output fees: Flat $0.042/M input token pricing with unmetered ($0.00) output tokens.
- Strict boundaries: Purpose-built for discrete decisions and routing; cannot generate freeform text, perform math, or reason about dates.
Jev vs. Gemini 3.8 Flash
We ran 385 banking queries across 77 intents through both Jev (jev-1.13.0) and Gemini 3.8 Flash structured JSON output. Jev was 6.4x faster and 85% cheaper, but Gemini resolved subtle boundary cases more accurately.
| Metric | TypeSafe Jev (jev-1.13.0) | Gemini 3.8 Flash (structured JSON) |
|---|---|---|
| Median latency (p50) | 188ms | 1,204ms |
| Tail latency (p95) | 320ms | 3,245ms |
| Top-1 accuracy | 79.5% | 83.6% |
| Cost per 1,000 decisions | $0.068 | $0.457 |
Intent Routing & Fallbacks
What happened when we tested 5,500 CLINC150 queries: why forced choice fails on unsupported inputs, and how an explicit fallback catches 76.8% of them.
Read the routing benchmark →Confidence & Calibration
How Jev’s confidence scores behave under real loads: sharp calibration when intents are distinct, overconfidence on subtle boundaries, and the tail risks.
Read the calibration analysis →Workload Cost Economics
How Jev’s $0.042/M input rate and unmetered output tokens model out at production volume compared against generative LLM structured outputs.
Inspect workload economics →Claude Opus 5.5: Specifications, Pricing & Benchmarks
Anthropic released Claude Opus 5.5 with a 1M-token context window and lower token pricing. We evaluate the 20% base price cut, Anthropic’s 40% task savings claim, and what independent evaluations currently show.
What is Claude Opus 5.5?
Anthropic’s new frontier model (claude-opus-5-5) brings 1M context, 128k output, $4/M input, and $20/M output token pricing. While Anthropic claims up to 40% operating cost reductions, the actual savings depend heavily on prompt caching, reasoning depth, and task structure.
- API Pricing: $4.00/M input tokens and $20.00/M output tokens (20% below Opus 5), with prompt-cache reads down 60% to $0.20/M.
- Context & Limits: 1,000,000-token input context and 128,000-token output ceiling; generally available on Google Cloud.
- Independent Benchmarks: Ranked #1 on the Artificial Analysis Intelligence Index (score 58 at max effort) and #1 on GDPval-AA v2.1 at max effort (Elo 1846). Scores drop with effort: 1692 at high effort ranks below other frontier configurations.
Choose, Build, and Run AI Systems
Tested patterns for evaluating model roles, structuring autonomous agent loops, and keeping workflows reliable.
How to Choose an AI Model
A practical decision guide across five model roles: deep reasoning, fast text generation, bounded decision models, lightweight embeddings, and local models.
Read the model selection guide →Production Coding Agent Workflow
A 10-stage lifecycle for autonomous coding agents: small diffs, strict compiler and linter gates, and independent verification passes.
Study the coding agent workflow →AI Agent Architecture
The eight subsystems an autonomous agent actually needs in production: context state, sandboxed tool execution, safety checks, and failure recovery.
Inspect the agent architecture →Jev Implementation & API Reference
Code examples, endpoint schemas, and primitive comparisons for integrating TypeSafe Jev into your stack.
How to Use Jev (Quickstart)
Python and TypeScript SDK setup, state payload formatting, and structuring affirmative question criteria.
Follow setup tutorial →Jev API Reference
Endpoint schemas for POST /v1/systemone, error codes (401, 422, 429, 529), and batching rules.
Choice vs. Score vs. Noul
Comparing return shapes, continuous ordinal distributions, and boolean probability calibration across Jev’s three primitives.
Compare decision primitives →Evidence Standards
How we strictly separate vendor documentation, our own benchmark data, hands-on operational findings, and inference.
Inspect our evidence standards →