Telnyx
OpenAI-compatible inference API — GLM-5.3, Kimi, DeepSeek, MiniMax
Telnyx Inference API provides OpenAI-compatible access to a curated set of open-weight and frontier models including GLM-5.3 Flash, Kimi K2.6, DeepSeek V4 Flash, and MiniMax-M3. PAYG pricing with no minimum spend. Telnyx is primarily known as a communications platform; the inference API is a newer addition to their product suite.
OpenAI-compatible inference API from a communications platform, with competitive rates on GLM-5.3 Flash and DeepSeek V4 Flash.
- OpenAI-compatible API — drop-in replacement for existing integrations
- Competitive pricing on GLM-5.3 Flash and DeepSeek V4 Flash
- PAYG with no minimum spend
- Prompt caching on GLM-5.3 Flash (cached input at $0.027/1M)
- Smaller model catalog vs major inference hubs
- Inference is a secondary product — communications is the core business
- Limited benchmark transparency vs dedicated inference providers
Telnyx Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
DeepSeek V4 Flash DeepSeek | Chat | efficient | $0.13/ $0.26 in / out per 1M EST Oct 2026 | — | — | — | 131K | — | ||
GLM-5.3 Flash GLM-5.3 | Chat | efficient | $0.14/ $0.45 in / out per 1M EST Oct 2026 | $0.03 | — | — | 131K | — | ||
MiniMax-M3 MiniMax | Chat | efficient | $0.27/ $1.10 in / out per 1M EST Oct 2026 | — | — | — | 131K | — | ||
Kimi K2.6 Kimi | Chat | advanced | $0.67/ $4.00 in / out per 1M EST Oct 2026 | — | — | — | 131K | — |
Telnyx — Frequently Asked Questions (FAQs)
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Telnyx is an inference API provider that hosts open-weight and third-party large language models. Telnyx Inference API provides OpenAI-compatible access to a curated set of open-weight and frontier models including GLM-5.3 Flash, Kimi K2.6, DeepSeek V4 Flash, and MiniMax-M3. PAYG pricing with no minimum spend. Telnyx is primarily known as a communications platform; the inference API is a newer addition to their product suite. Common use cases include Chat, Coding, Cost-sensitive workloads. Headquartered in Chicago, IL, founded 2009. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Telnyx vs other LLM providers
Telnyx competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. OpenAI-compatible inference API from a communications platform, with competitive rates on GLM-5.3 Flash and DeepSeek V4 Flash. Use the LLM provider comparison tool to see Telnyx token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Telnyx?
Telnyx stands out because: OpenAI-compatible inference API from a communications platform, with competitive rates on GLM-5.3 Flash and DeepSeek V4 Flash.. The API currently offers 4 models, spanning 1 advanced, 3 efficient tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Telnyx token pricing
Telnyx charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.13/1M tokens. Prices shown are sourced from Telnyx's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Telnyx context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Telnyx's largest context window is 131K tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Telnyx makes sense
Managed inference APIs like Telnyx eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Telnyx API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Telnyx token price data is available in the LLM price history charts.
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