GLM 5.2 Inference benchmarks across 4 providers

GLM-5.2 is now available on Telnyx Inference as zai-org/GLM-5.2-FP8, giving developers access to a large open-source reasoning model with a 1M context window through an OpenAI-compatible API. Pricing is $1.40 per 1M input tokens, $4.40 per 1M output tokens, and $0.26 per 1M cached input tokens.

That context window matters for real applications: support copilots that need long case histories, document-heavy workflows, agentic coding tools, retrieval systems with large context packs, and internal assistants that need to reason across dense operational data. But model quality is only one part of the developer experience. For production apps, latency determines whether the product feels interactive, useful, or painfully slow.

This is also where Telnyx is structurally different. We host these models on our own GPU infrastructure, end to end—not rented cloud GPUs with a provider markup on every token. Telnyx moved its own AI workloads onto open-weight models running on its own hardware and cut AI spend by about 90%. The pricing above is that same structural advantage passed through to developers.

To understand how GLM-5.2 behaves in practice, we benchmarked Telnyx Inference against Baseten, Together AI, and Fireworks using the same streamed prompt profiles from our prior inference tests.

Benchmarking GLM-5.2 on Telnyx, Baseten, Together AI, and Fireworks

We tested exact GLM-5.2 availability across these four providers:

The benchmark used six prompt profiles: 1k, 10k, and 100k input-token targets, each paired with short and long output targets. Each provider/profile cell was run 10 times over streamed responses.

We focused on two user-facing latency metrics:

• E2E latency: how long it takes to receive the full response.
• TTFT: how long it takes to receive the first streamed token.

For developers, these map to different product decisions. E2E latency matters when the user needs the complete answer before taking the next step. TTFT matters when you want the interface to feel alive immediately, especially in chat, voice, or agent workflows.

Inference benchmarking results

Telnyx had the lowest overall p50 E2E latency at 6.00s, with Baseten very close at 6.19s. Together AI landed at 7.49s, while Fireworks was slower overall at 14.98s.

That makes E2E latency the headline result: Telnyx delivered the fastest median full-response time across the benchmark. For production applications where the full output is needed before the next action—summaries, document workflows, internal tools, and agent steps—that is the latency users actually feel.

The shape of the results is useful:

• Telnyx and Baseten were strongest on full-response latency.
• Telnyx performed especially well on long-output profiles compared with the other providers.
• Fireworks completed the run cleanly, but had higher E2E latency and more variance on long-output profiles.
• Together AI showed strong first-token responsiveness and measurable throughput, but had a few large E2E outliers.

One caveat sits inside the E2E numbers: a few Baseten responses finished before the requested output length, which can make its E2E latency look faster on those rows. The per-profile tables in the report show exactly where, so the comparison stays honest at full output length.

For TTFT, Baseten led at 0.76s, followed closely by Together AI at 0.79s. Telnyx came in at 1.12s, and Fireworks at 1.28s.

TTFT matters most when you want a streamed interface to feel responsive immediately. E2E latency matters when the user or system needs the complete answer before taking the next step. In this benchmark, Telnyx was strongest on the full-response metric while still delivering first tokens in just over one second.

The practical takeaway: Telnyx GLM-5.2 is a strong fit when your application needs the full answer quickly, especially when prompts or generated responses get longer.

Build with Telnyx Inference

Telnyx Inference is OpenAI-compatible, so you can use the OpenAI Python SDK and swap the base_url. The Telnyx docs show the same pattern in the Inference API quickstart, and GLM-5.2 is listed on the available models page.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("TELNYX_API_KEY"),
    base_url="https://api.telnyx.com/v2/ai/openai",
)

stream = client.chat.completions.create(
    model="zai-org/GLM-5.2-FP8",
    messages=[
        {
            "role": "system",
            "content": "You are a precise technical assistant.",
        },
        {
            "role": "user",
            "content": "Summarize this incident report and list the next actions...",
        },
    ],
    temperature=0,
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta
    if getattr(delta, "content", None):
        print(delta.content, end="", flush=True)

Streaming is worth using even when you care about the final answer. It lets your app show progress immediately, measure TTFT, and avoid making users stare at a blank screen during long generations.

Measure the latency your users feel

If you are building on an inference API, measure at least two timings in your app:

import time

start = time.perf_counter()
first_token_at = None
full_text = []

stream = client.chat.completions.create(
    model="zai-org/GLM-5.2-FP8",
    messages=[{"role": "user", "content": "Explain this contract in plain English."}],
    temperature=0,
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta
    content = getattr(delta, "content", None)
    if not content:
        continue

    if first_token_at is None:
        first_token_at = time.perf_counter()

    full_text.append(content)

end = time.perf_counter()

ttft_seconds = first_token_at - start if first_token_at else None
e2e_seconds = end - start

print({
    "ttft_seconds": ttft_seconds,
    "e2e_seconds": e2e_seconds,
    "output_chars": len("".join(full_text)),
})

Use TTFT to tune the feel of chat and voice experiences. Use E2E latency to tune workflows where the full output is required before the next action.

Design patterns that work well with GLM-5.2

Here are a few app patterns where GLM-5.2 on Telnyx Inference is a natural fit:

• Long-context support copilots: include full tickets, logs, account history, and runbooks in a single request. The 1M context window and cached-input pricing at $0.26 per 1M tokens make large context practical instead of something you ration.
• Document-heavy workflows: summarize, classify, and extract structured fields from large documents or document bundles on owned infrastructure priced directly per token.
• Agent planning: give the model a large working set of context, then ask it to produce a plan, tool calls, or next actions. Predictable E2E latency helps keep multi-step agents from stalling.
• Internal engineering assistants: reason over code snippets, incident notes, traces, and architecture docs using the same open-weight, owned-infrastructure pattern Telnyx uses for its own AI workloads.

For production UX, stream the response, show partial output, and add retry logic for transient provider errors. If your app depends on long generations, store request metadata such as prompt profile, output length, TTFT, E2E latency, and error type so you can debug real customer behavior instead of relying only on synthetic benchmarks.

Methodology notes

The clean dataset uses 240 successful requests, with 10 streamed runs per provider and profile. A small number of transient errors during the raw run were recovered on a retry pass before the dataset was finalized.

Throughput was only measurable for Together AI and Baseten in this run because Telnyx and Fireworks did not return streamed token usage for this model. We kept throughput out of the headline comparison and focused the main analysis on E2E latency and TTFT.

Takeaway

For developers building latency-sensitive applications with GLM-5.2, Telnyx Inference looks strongest when the full response matters. The API is OpenAI-compatible, the model supports a large context window, and the p50 E2E result was the best in this first-pass benchmark.

The next step is to benchmark against your own workload. Use the same basic structure: realistic prompts, streamed responses, TTFT, E2E latency, output length, and retry behavior. Synthetic benchmarks are useful, but your product’s prompt shape is what ultimately decides the user experience.