GLM-5.2 vs GPT-5.5 Cost: Per-Token Math at 10K/100K/1M Req/Day (2026)

TL;DR — At ofox.io's listed pricing, GLM-5.2 costs $1.4 input / $4.4 output per million tokens; GPT-5.5 sits at $5 / $30. Blended at a 2:1 input-to-output ratio, that is $2.40 vs $13.33 per million tokens — a 5.56x cost ratio. At 100K requests per day on 3K-token prompts, you spend roughly $720/day on GLM-5.2 versus $4,000/day on GPT-5.5 — about $21,600 vs $120,000 per month. Prompt caching helps both but doesn't close the gap. Both models are on the same OpenAI-compatible endpoint at ofox.io so the comparison is a one-line model swap.

GPT-5.5's per-token cost is 5.56x GLM-5.2's at a typical coding mix — and 6.82x on pure output tokens. The question stopped being whether GLM-5.2 is "good enough"; it became which workload still earns the GPT-5.5 premium.

If you want to skip the math and just A/B both models on your own workload, ofox.io hosts both z-ai/glm-5.2 and openai/gpt-5.5 on the same key — pay-as-you-go, no monthly fee, and the same SDK shape as the OpenAI Python client. The full math below uses ofox's listed per-token rates verified June 21, 2026.

TL;DR: Which One Should You Pick?

The honest verdict for most 2026 coding teams: route the cost-sensitive default traffic to z-ai/glm-5.2, keep openai/gpt-5.5 on the Codex CLI / interactive surface, escalate the hardest 10% to Claude. The two-model split below covers the realistic 80% of your traffic without a vendor migration.

What Each Model Ships on ofox

Both models live on api.ofox.io/v1 under the OpenAI-compatible protocol, and on the Anthropic-protocol endpoint for Claude Code drop-in use. The boring numbers, verified against the ofox model catalog on June 21, 2026:

Two things to call out from the spec sheet. First, the context windows and output ceilings are effectively identical — both list a 1M context and a 128K max-output cap, so neither model lets you emit a larger single-call patch than the other; on long refactor jobs the deciding factor is per-token cost, not output capacity. Second, GPT-5.5 on ofox is Azure-backed. That is the procurement story for shops already inside the Microsoft compliance perimeter; it does not change the listed rate card visible to most accounts but it does mean the upstream is Microsoft, not OpenAI direct.

For the full GLM-5.2 access path — pricing tiers, MIT weights timeline, Z.ai's own Coding Plan — see our GLM-5.2 access guide. For the GPT-5.5 coding benchmark picture against the other 2026 frontier models, see the MiniMax M3 vs GPT-5.5 SWE-Bench breakdown.

Real Per-Token Math: Three Workload Scenarios

Sticker pricing is straightforward. The interesting number is what the invoice looks like at your actual scale. We use three scenarios across the realistic volume range that teams hit in production.

Assumption block (held constant across all three):

• 3,000 tokens per request, split 2:1 input to output (2K in, 1K out)
• 30 days per month
• No cache hits in the headline number (we add cache impact in the next section)
• Web search add-on excluded

Light: 10K requests per day

Roughly the shape of a small team running a single coding agent at moderate intensity, or a side project at scale.

• Daily input tokens: 10K × 2K = 20M
• Daily output tokens: 10K × 1K = 10M

Mid: 100K requests per day

The shape of a 10-engineer team running coding agents full time, or a product feature that exposes the model to end-users at moderate concurrency.

• Daily input tokens: 100K × 2K = 200M
• Daily output tokens: 100K × 1K = 100M

Heavy: 1M requests per day

The shape of a production agent fleet, a developer-tooling SaaS at scale, or an internal platform exposed to a four-figure-engineer org.

• Daily input tokens: 1M × 2K = 2B
• Daily output tokens: 1M × 1K = 1B

The 5.56x ratio holds at every volume tier — only the absolute spend scales. At light volume that is a useful saving; at mid volume it pays for two senior engineers per month; at heavy volume it is the difference between a feature shipping and a feature being killed for unit-economics reasons.

These tables hold for the standard 2:1 input-to-output mix. The ratio drifts with workload shape: at 1:1 (chat-style turns) the cost ratio is 6.03x; at 1:3 output-heavy (code generation from a short prompt) the ratio is 6.51x; at 3:1 input-heavy (long-context summarization) the ratio narrows to 5.23x because GLM-5.2's per-input-token discount (3.57x cheaper input) is smaller than its per-output-token discount (6.82x cheaper output). Output-dominated workloads tilt further toward GLM-5.2; input-dominated workloads tilt less hard but still favor GLM at every realistic mix.

Cache Impact: How Far Does Prompt Caching Close the Gap?

Both models bill cache reads below the full input rate: GLM-5.2 at $0.26/M (an 81% input discount), GPT-5.5 at $0.50/M (a 90% input discount). Cache hit rates above 50% are realistic for code-review workloads where the codebase context repeats across requests. Here is what 50% input cache hit does to the blended cost.

At 50% input cache hit (half of input tokens served from cache, output unchanged):

At 100% input cache hit (every input token cached):

Two reads on this. First, cache saves more absolute dollars on GPT-5.5 per cached token — you avoid $4.50 per cached million on GPT-5.5 versus $1.14 on GLM-5.2. If your CFO scores the cache program by raw dollars saved, GPT-5.5 wins. Second, cache saves a larger share of GLM-5.2's total bill — because input is a bigger fraction of GLM-5.2's blended cost, cutting input costs has a bigger proportional effect. At 100% input cache hit, GLM drops 31.7% of its blended bill; GPT-5.5 drops 22.5%.

The net result is that GLM-5.2 stays cheaper at every cache hit rate point. The cost ratio actually widens slightly as cache hit rate climbs — from 5.56x without cache to 5.86x at 50% input cache hit to 6.30x at 100% input cache hit. That sounds counterintuitive, but the math is straightforward: cache eats a larger share of GLM-5.2's blended bill than of GPT-5.5's, so GLM's bill shrinks faster in percentage terms. Prompt caching is a uniform discount on input only; it does not change the GPT-5.5 output rate, and output is where the absolute dollar gap lives.

When GLM-5.2 Wins (and When the Benchmark Gap Is Acceptable)

Five workloads where GLM-5.2 is the obviously correct routing decision:

1. Batch code review and async refactor sweeps. Overnight dependency upgrades, doc generation, batched lint fixes — work where total token spend dominates and individual-request latency does not matter. The 5.56x cost gap compounds across thousands of requests per night.
2. Long-context refactor jobs. GLM-5.2's 1M context lets you submit an entire mid-sized module in one prompt. Its 128K output cap is identical to GPT-5.5's, so very large rewrites still chunk on both models — but GLM-5.2 emits the same patches at 5.56x lower per-token cost, and its input is 3.57x cheaper, which dominates on input-heavy refactor passes.
3. Output-heavy code generation pipelines. Per-output-token cost is the differentiator at 6.82x. If your agent emits more code than it reads (test generation, scaffolding, codemod application), GLM-5.2 disproportionately wins.
4. High-cache-hit workloads. Code-review agents reusing the same codebase context, RAG pipelines with stable corpora — GLM-5.2's cache read at $0.26/M is half of GPT-5.5's $0.50/M, and the proportional cache benefit on GLM is larger.
5. Open-weight insurance. MIT-licensed weights mean if Z.ai changes hosted pricing or terms, you can fall back to self-hosting on the same model. GPT-5.5 has no on-prem path. Even if you never deploy the weights, the option value is real.

The honest qualifier: the benchmark gap to GPT-5.5 is real on Terminal-Bench-style agentic work. Z.ai had not published SWE-Bench Verified scores at GLM-5.2's launch, and independent third-party benchmark numbers were pending as of mid-June 2026. If your workload depends on the multi-step shell agentic loop that Terminal-Bench measures, GPT-5.5 still leads — for everything else, the cost case is decisive.

When GPT-5.5 Still Makes Sense

Three workloads where the 5.56x premium earns its keep:

1. Codex CLI is your primary surface. OpenAI's terminal agent is tuned against GPT-5.5 at the protocol level — file handles, shell history, multi-turn recovery from failed commands. The Terminal-Bench 2.1 score (82.7%) reflects integration depth as much as model capability. Swapping the model behind Codex is not a free move.
2. Latency-sensitive interactive coding. Pair-programming flows where every extra second of first-token latency hurts adoption. GPT-5.5 is tuned for short prompts and fast first-token; on a 5K-token interactive prompt, GPT-5.5 typically wins the latency comparison.
3. Azure-backed procurement. ofox's GPT-5.5 line is Azure-backed, which closes the procurement story without a new vendor review for shops already inside Microsoft compliance. The procurement cost of adding a new model vendor often exceeds the per-token savings for teams below a few hundred thousand tokens per day.

The fourth scenario is mixed-workload reasoning load — if your coding agent occasionally writes architecture summaries, postmortems, or research briefs, GPT-5.5's general reasoning ceiling is higher than GLM-5.2's. That said, for purely coding workloads, the cost case for GLM-5.2 dominates.

A/B Routing Pattern via ofox: One Key, One Endpoint, Two Models

Both z-ai/glm-5.2 and openai/gpt-5.5 are live on https://api.ofox.io/v1 under the OpenAI-compatible protocol. The model swap is a single string change. The smallest useful A/B harness:

Python — A/B both models in one loop

from openai import OpenAI
import os, time

client = OpenAI(base_url="https://api.ofox.io/v1", api_key=os.environ["OFOX_API_KEY"])

prompt = "Refactor this Python function to use async/await and return early on empty list: ..."

for model in ["z-ai/glm-5.2", "openai/gpt-5.5"]:
    t0 = time.time()
    resp = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    )
    elapsed = time.time() - t0
    print(f"{model}: {elapsed:.1f}s, {resp.usage.total_tokens} tokens")
    print(resp.choices[0].message.content[:200])

That gives you raw latency, total token count, and side-by-side output on your own task. Run it across 20-30 representative cases from your real workload — that is the only honest input to a routing decision.

Node — same shape

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.ofox.io/v1",
  apiKey: process.env.OFOX_API_KEY,
});

const prompt = "Refactor this Python function to use async/await and return early on empty list: ...";

for (const model of ["z-ai/glm-5.2", "openai/gpt-5.5"]) {
  const t0 = Date.now();
  const resp = await client.chat.completions.create({
    model,
    messages: [{ role: "user", content: prompt }],
  });
  console.log(`${model}: ${(Date.now() - t0) / 1000}s, ${resp.usage.total_tokens} tokens`);
  console.log(resp.choices[0].message.content.slice(0, 200));
}

Production routing — single-line model swap

The same SDK call, the same key, the same billing line. To route the cost-sensitive half of your traffic to GLM-5.2 and keep the interactive half on GPT-5.5:

def pick_model(request_type: str) -> str:
    if request_type in {"batch_refactor", "code_review", "doc_generation"}:
        return "z-ai/glm-5.2"
    return "openai/gpt-5.5"

resp = client.chat.completions.create(
    model=pick_model(request_type),
    messages=messages,
)

No migration, no new key, no separate billing reconciliation. The model column on your invoice tells you what each request cost; the routing function is one place to tune the split. For the broader pattern of routing across the full ofox catalog — including Claude for escalations — see our $30 AI coding stack guide.

Sources & Pricing References

• ofox.io model catalog: z-ai/glm-5.2 — input $1.4/M, output $4.4/M, cache $0.26/M, 1M context, 128K max output, listed June 16, 2026 (verified June 21, 2026)
• ofox.io model catalog: openai/gpt-5.5 — input $5/M, output $30/M, cache $0.5/M, 1M context (922K in / 128K out), listed April 24, 2026, Azure-backed (verified June 21, 2026)
• GLM-5.2 access guide — pricing tiers, MIT weights, Z.ai Coding Plan
• MiniMax M3 vs GPT-5.5 SWE-Bench Pro coding benchmark — companion benchmark-led comparison
• Vellum — GPT-5.5 reference — Terminal-Bench 2.1 score 82.7%, output token rate $30/M confirmed

At a 5.56x cost ratio that holds across volume tiers and a 6.82x gap on pure output tokens, the routing question is no longer "is GLM-5.2 good enough" — it is "which workload still justifies paying the GPT-5.5 premium," and "Codex CLI shop" is the cleanest honest answer.

Originally published on ofox.ai/blog.