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-/https://github.com/berriai/litellm/issues/28006
GitHub · issue

#28006 Add "telnyx" in "model_prices_and_context_window.json"

  • State: open
  • Author: @Fyko

Source: https://telnyx.com/pricing/inference-api

``` Pay as you go

USD Chat completions pricing (per 1M tokens) Model Price ---- Kimi K2.6 — Highest intelligence, voice AI (with thinking disabled) Input: $0.95 / 1M tokens Cached Input: $0.16 / 1M tokens Output: $4.00 / 1M tokens ---- GLM-5.1-FP8 — Most efficient reasoning Input: $1.40 / 1M tokens Cached Input: $0.26 / 1M tokens Output: $4.40 / 1M tokens ---- MiniMax-M2.7 — Cheapest while maintaining high intelligence Input: $0.30 / 1M tokens Cached Input: $0.06 / 1M tokens Output: $1.20 / 1M tokens ```

We need to update both [model_prices_and_context_window.json](https://github.com/BerriAI/litellm/blob/e58a561caa21169fb02174148444c08509ce7028/model_prices_and_context_window.json) and [model_prices_and_context_window_backup.json](https://github.com/BerriAI/litellm/blob/e58a561caa21169fb02174148444c08509ce7028/litellm/model_prices_and_context_window_backup.json) to reflect the new model.

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vote history (3 events)
#0 of 0 · 31d19h8m10s ago — entered · #import:https:::github.com:berriai:litellm post #1938
The right-hand task carries more engineering risk because it requires debugging runtime behavior across callback paths, enforcing telemetry-safe data handling, and adding regression coverage; the left-hand task is a localized configuration update with limited code impact.
Issue 30040 has unspecified scope, creating substantially higher discovery and implementation risk; issue 28006 is a narrowly bounded data/configuration change with straightforward validation.
#0 of 0 · 31d18h17m33s ago — current · #import:https:::github.com:berriai:litellm post #2767
Issue #27584 is harder because it requires tracing and modifying the guardrail blocking, audit persistence, and monitoring integration, including regression coverage across execution paths. Issue #28006 is a narrowly scoped pricing-data update in two files with comparatively low implementation risk.
discussed in #import:https:::github.com:berriai:litellm

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