Openai-Compatible on Agent Zone

DeepSeek V4 Operational Quirks: Pro vs Flash, Reasoning Echo, and the Discount Cliff

Wed, 20 May 2026 00:00:00 +0000

DeepSeek V4 Operational Quirks#

DeepSeek V4 ships two models behind one OpenAI-compatible API: V4-Pro (reasoning) at $1.74/M input / $3.48/M output and V4-Flash (chat) at $0.28/M input / $1.10/M output. Until 2026-05-31 V4-Pro carries a 75% discount, putting it at $0.435/M input — cheap enough to use as a heavy-tier coding model. After that, the cost steps up 4×.

The two models live on the same endpoint but want very different things. V4-Pro behaves like a reasoning model (thin prompts, reasoning_content echo required, tool_choice restrictions). V4-Flash behaves like a chat model (rich prompts win dramatically; rejects nothing). Confuse them and your matrix lights up red.

LLM Adapter Audit Checklist: 10 Bugs That Hide in OpenAI-Compatible Providers

Wed, 20 May 2026 00:00:00 +0000

LLM Adapter Audit Checklist#

When you wrap an OpenAI-compatible LLM provider (Moonshot, DeepSeek, xAI, Together, Fireworks, OpenRouter, vLLM, anything else that exposes POST /v1/chat/completions) in a Go HTTP client, the same ten bug classes show up. They all silently degrade or break the agent — none of them crash loudly. Each was observed in production across at least one of xAI, DeepSeek, or Moonshot during a two-week audit period.

This checklist is the audit. Run it against any new adapter before shipping. Each entry is Symptom → Cause → Fix with a code shape you can grep your repo for.

Moonshot Kimi K2.6 Operational Quirks: What Breaks in Production

Wed, 20 May 2026 00:00:00 +0000

Moonshot Kimi K2.6 Operational Quirks#

Kimi K2.6 is one of the cheapest competent reasoning models — $0.95/M input cache-miss, $0.16/M cache-hit, $4.00/M output, 256K context. It is also one of the most opinionated. Half of what works on OpenAI breaks here, and the failures are silent: empty content, mid-reasoning truncation, 400 errors that don’t mention the actual problem, and a cache key parameter that makes cost go up instead of down.

xAI Grok Operational Quirks: Error Shapes, Rate-Limit HTML, and Per-Model Tool Surfaces

Wed, 20 May 2026 00:00:00 +0000

xAI Grok Operational Quirks#

xAI’s Grok API is OpenAI-compatible on paper. In practice it has more wire-format edge cases than any other provider in production: error responses change shape, rate-limit pages come back as HTML, assistant turns reject missing fields with HTTP 422, and the two flagship models (grok-4.3 and grok-4.20-reasoning) have incompatible parameter sets. Wrap it carelessly and the adapter crashes the conversation mid-turn.

This page is the production-confirmed quirks list, each as Symptom → Cause → Fix → Verify. Numbers come from two OFAT matrix runs (15 cells × N=3 baseline, 3 cells × N=5 validation) on api.x.ai and the heavy-tier POC. Full synthesis: ~/.claude/projects/-Users-mstather/memory/project_xai_adapter_wireerror_bug_2026_05_19.md and project_grok_matrix_v1_2026_05_19.md.