{"uid":"cap_kQSW8Cfs2FlnheVesA1S5","slug":"telnyx-inference-qwen3-235b-a22b-chat-completions-large-context-1f9c4378","name":"Telnyx Inference – Qwen3-235B-A22B Chat Completions (Large Context)","description":"Chat completions via Telnyx Inference (Qwen/Qwen3-235B-A22B). OpenAI-compatible. $0.010497 USDC per call. 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Use Qwen3-235B on Telnyx to review it and flag any clauses that limit indemnification — keep the temperature at 0.1 so it stays precise and cap the output at 6000 tokens."},{"title":"Multi-turn coding assistant session","prompt":"Set up a multi-turn conversation with Qwen3-235B on Telnyx Inference: first message is a system prompt saying you are an expert Python engineer, then ask it to design a Redis-backed task queue with retry logic — temperature 0.7, up to 4096 tokens."},{"title":"Tool-augmented research agent","prompt":"I want to run an agentic research loop using Qwen3-235B via Telnyx — give it these three tool definitions for web search, calculator, and calendar lookup, then ask it to plan a product launch timeline for Q4 with temperature 0.5 and max 2048 tokens."}],"resultDescription":"Returns an OpenAI-compatible chat completion response object containing the assistant's generated message text, finish reason, token usage statistics, and any tool call objects if tools were provided. The model used is Qwen/Qwen3-235B-A22B.","failureModes":["Payment failure via x402 protocol if wallet has insufficient USDC balance","Context length exceeded if total tokens across messages surpass model limits (up to 200,000 chars per message, 32 messages max)","Rate limiting or throttling on high-frequency requests","Model returns truncated output if max_tokens is set too low","Invalid tool schema causes tool-call parsing errors","Temperature out of [0,2] range returns validation error","Non-1 value for n parameter rejected by fixed constraint"],"whenToPreferThis":"Choose this endpoint when you need a very large open-weights model (235B parameters, Mixture-of-Experts architecture) for complex reasoning, long-document analysis, or nuanced instruction following, and want OpenAI-compatible API semantics with per-call micropayment billing via x402. Prefer it over smaller Telnyx Inference models when task complexity demands frontier-scale capability and over proprietary APIs when you want transparent per-call pricing without subscription overhead.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:35:50.567Z","isFirstParty":false,"canonicalSlug":"telnyx-inference-qwen3-235b-a22b-chat-completions-large-context-1f9c4378"}