---
title: DeepSeek V4 Flash Inference Benchmarks on Telnyx
canonical: https://telnyx.com/resources/deepseek-v4-flash-benchmarks
---
# DeepSeek V4 Flash Inference Benchmarks on Telnyx

[Back to blog](https://telnyx.com/resources)

**Inference**

# DeepSeek V4 Flash inference benchmarks on Telnyx

DeepSeek V4 Flash inference benchmarks comparing Telnyx StatefulActor, Baseten, Fireworks, and Together AI on E2E latency and throughput.

By [Sonam Gupta, PhD](https://telnyx.com/authors/sonam-gupta-phd)

When developers evaluate an [inference provider](https://telnyx.com/products/inference), model availability is only the first question. The more practical question is how the model behaves under real application-shaped workloads: how long the full answer takes and how steady it looks as prompts and outputs get larger.

To test that, we ran a repeatable benchmark for `deepseek-ai/DeepSeek-V4-Flash-0731` using [Telnyx stateful actors](https://developers.telnyx.com/docs/edge-compute/stateful-actors) as the benchmark orchestrator. The actor owned the run, called each provider endpoint, streamed model responses, recorded timing metadata, and exported the results.

The value of this pattern is durability. A [stateful actor](https://telnyx.com/products/stateful-actors) can keep benchmark state and progress even when the client that started the job is no longer actively connected. The same pattern applies to eval jobs, batch enrichment, document processing, voice-agent analysis, and other long-running AI workflows.

## What we measured

We focused on two metrics developers feel directly in production apps:

* **E2E latency:** request start to final streamed token. Lower is better.
* **Throughput:** generated tokens per second after streaming begins. Higher is better.

E2E latency matters when the app needs the complete answer before taking the next step. Throughput matters once the model is already responding and the user is waiting for the rest of the answer.

## Benchmark setup

The benchmark used six prompt profiles:

* 1k input / \~100 output
* 10k input / \~100 output
* 100k input / \~100 output
* 1k input / \~1k output
* 10k input / \~1k output
* 100k input / \~1k output

Each provider/model/profile combination ran 10 times with streaming enabled and temperature set to 0\. This rerun also explicitly disabled reasoning where provider APIs accepted a reasoning-control field, and all successful rows emitted `0` reasoning characters.

Baseten returned six `429 Rate limit exceeded` responses on long-context rows. Those failed rows were excluded from latency percentiles.

## Throughput

For generated-token throughput, higher is better. Telnyx stateful had the strongest p50 throughput in this rerun.

That distinction matters for longer completions. Throughput controls how quickly the response arrives once the model starts generating, and the gap widens as output length grows.

## Reliability and tail latency

Telnyx stateful completed all 60 rows. Baseten had the fastest p50 E2E, but six Baseten rows returned `429 Rate limit exceeded` on long-context profiles and were excluded from latency percentiles.

Among providers with 60/60 successful rows, Telnyx stateful had the lowest p95 E2E at 12.24s. Fireworks was close at 14.49s, while Together AI had a longer tail at 24.91s.

## E2E latency

Across clean rows, Baseten had the fastest p50 E2E latency in this rerun. Telnyx stateful was second on p50 E2E, but had the lowest p95 E2E among providers that completed all 60 rows.

The profile chart is the better read than a single overall number. Short-output requests stay closer together, while \~1k-output requests separate providers more clearly. That is expected because output length usually dominates E2E latency.

## Why stateful actors fit this workflow

A benchmark is a useful example of a broader production pattern. Many AI workflows are not just one request and one response. They need to start a job, track progress, retry individual steps, store structured results, and export the final output.

[Telnyx stateful actors](https://developers.telnyx.com/docs/edge-compute/stateful-actors) fit that shape. In this benchmark, the actor:

* owned the run state,
* tracked completion across hundreds of model calls,
* streamed responses so E2E latency could be measured accurately,
* stored success and failure rows separately,
* exported clean CSV and report artifacts.

If you are building with [Telnyx inference](https://developers.telnyx.com/docs/inference/getting-started), this means inference can sit next to durable application logic. You can call an OpenAI-compatible inference endpoint, stream the response, measure user-facing latency, and keep workflow state inside the Telnyx edge compute layer.

Start here:

* [Telnyx inference getting started](https://developers.telnyx.com/docs/inference/getting-started)
* [Telnyx inference models](https://developers.telnyx.com/docs/inference/models)
* [Telnyx stateful actors](https://developers.telnyx.com/docs/edge-compute/stateful-actors)

## Methodology notes

Benchmark traffic was initiated from a Telnyx stateful actor using the regional `us-east-2` invoke URL. The benchmark called `https://api.telnyx.com/v2/ai` for Telnyx inference and each competitor through its own API path. We did not independently log the GPU-serving region for the Telnyx inference calls, so the results should not be described as proving same-region or zero-hop routing between the actor and model-serving layer.

This rerun controlled reasoning output by disabling reasoning behavior where provider APIs accepted that control. All successful rows emitted `0` reasoning characters. DSpark/speculative-decoding state was not independently toggled in this run, so throughput should be described as the observed result for Telnyx's current serving configuration, not as an architecture-only measurement.

## Take DeepSeek V4 Flash to production

High throughput and low tail latency with Telnyx inference.

[See inference pricing](https://telnyx.com/pricing/inference-api)

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Sonam Gupta, PhD

Developer Evangelist

Sonam is a San Francisco-based developer advocate, originally from India. She has completed 2 Master's Degrees and her PhD in Data Science from the Harrisburg University of Science & Technology. Previously, Sonam worked for the startups Ozmosi and aiXplain. In her free time, you 

will find Sonam dancing, writing, and exploring local coffee shops.

\[...\]

## Jump to:

[What we measured](#what-we-measured)[Benchmark setup](#benchmark-setup)[Throughput](#throughput)[Reliability and tail latency](#reliability-and-tail-latency)[E2E latency](#e2e-latency)[Why stateful actors fit this workflow](#why-stateful-actors-fit-this-workflow)[Methodology notes](#methodology-notes)[Take DeepSeek V4 Flash to production](#take-deepseek-v4-flash-to-production)

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