GLM 5.3 Flash vs DeepSeek V4.1 Flash: API, Price, Cost per Task

If you are choosing a cheap model for an agent or chatbot right now, the question I keep seeing in search is GLM 5.3 Flash vs DeepSeek V4.1 Flash. Both are open-weight MoE models under the MIT licence, both have a 1M-token context window, and both list input at $0.15 per million tokens. So which one is actually cheaper for your workload, and what do you change in code to switch? This guide compares the API, prices (including DeepSeek's peak hours), context, licence and a worked cost per task, with a small Python client that runs either model from one environment variable.

GLM 5.3 Flash vs DeepSeek V4.1 Flash at a glance

Sources: Z.ai model page, GLM-5.3-Flash model card, DeepSeek models and pricing, DeepSeek thinking mode, DeepSeek-V4.1-Flash-1017 model card.

The two architectures go after the same goal from different directions. Z.ai cut the active parameters and layers (18B active and 45 layers, against 32B and 92 for full GLM-5.3) and mixed linear with sparse attention to make 1M context cheap to serve. DeepSeek split the model into a "Causal Encoder-Decoder" with 8B active parameters for reading input and 16B for writing output, and says its KV cache needs a quarter of the HBM of the previous generation. You do not need to care about either design to call the API, but it explains why both vendors can price input this low.

Pricing: same input price, very different cache and output

Z.ai prices come from its pricing page, which also says cached input storage is "limited-time free". DeepSeek prices come from its models and pricing page.

Three things stand out:

• Cache hits: DeepSeek charges $0.003 off-peak against Z.ai's $0.03, ten times less. Agent loops resend the same long prefix on every turn, so this matters more than the headline input price.
• Output: GLM-5.3-Flash is cheaper at $0.50 against $0.60 off-peak and $1.20 at peak. Short chat replies are output-heavy, so GLM can win there.
• Time of day: DeepSeek's prices double at peak. GLM's price is the same around the clock.

GLM 5.3 Flash vs full GLM 5.3

A lot of searches ask how the Flash model compares with full GLM-5.3. On price, Flash is about 9 times cheaper on input ($0.15 against $1.40) and 8.8 times cheaper on output ($0.50 against $4.40). On capability, Z.ai's own numbers put Flash well above GLM-5.2 (63.4 against 46.2 on DeepSWE v1.1) and say it "approaches" Claude Opus 4.8 overall. Flash is also natively multimodal, while Mistral lists full GLM-5.3 as a text model. I would start on Flash and only move a task to full GLM-5.3 if a test set shows it failing.

Is GLM 5.3 Flash free?

Not on the API. Z.ai's free models are GLM-4.7-Flash and GLM-4.5-Flash; GLM-5.3-Flash is billed at the prices above. You can get it in two other ways: the GLM Coding Plan (from $18 a month according to the quick start), which gives Flash three times the quota of GLM-5.3, or by downloading the MIT-licensed weights and running them yourself on hardware you pay for.

Cost per task: a worked example

List prices only tell you so much, so here are two workloads with the arithmetic shown. Reasoning tokens count toward output, so estimate output with thinking included.

Scenario A: one coding-agent task. 40 turns, 2,000,000 input tokens in total with 90% served from cache (1,800,000 cached, 200,000 not), and 40,000 output tokens.

Scenario B: 1,000 support-bot replies. Each reply has 3,000 input tokens (a 2,000-token cached system prompt plus 1,000 new tokens) and 800 output tokens. In total: 1,000,000 uncached, 2,000,000 cached, 800,000 output.

Token counts are in millions and prices are per million.

What this tells me:

• For cache-heavy agent loops, DeepSeek off-peak is about 43% cheaper than GLM-5.3-Flash ($0.059 against $0.104), almost all because of the cache-hit price.
• For short, output-heavy chat, GLM-5.3-Flash is slightly cheaper than DeepSeek off-peak and less than half the price of DeepSeek at peak.
• If your traffic is mostly Sri Lankan or Indian business hours, DeepSeek's peak window covers a large part of it. A user-facing bot running at 10 AM Colombo time pays peak rates.

The caveat is that token counts differ by model. A model that thinks longer or needs more turns can erase a price gap, so run your own task set and read the usage fields (the code below does this) before you commit.

Quality: what the vendors report

I have not found an independent benchmark that tests these two models head to head under the same harness, so treat these as vendor claims:

• Z.ai reports GLM-5.3-Flash at 63.4 on DeepSWE v1.1 (mini-swe-agent harness) and 57 on the Artificial Analysis Intelligence Index v4.1.1 at "$0.045 per task (discounted)" (Z.ai docs).
• DeepSeek reports V4.1-Flash-1017 at 77.0 on DeepSWE v1.1 (Mini-SWE-V2 harness) and 42.3 on Terminal-Bench 4.0, and lists full GLM-5.3 at 66.9 on the same DeepSWE table (model card).

The harnesses differ, so the 63.4 and 77.0 are not a fair head-to-head. Where the models clearly differ is input type: GLM-5.3-Flash accepts video and files as well as images, which matters if your agent reads screen recordings or PDFs. For a broader view of where open models sit, see my Mistral Large 4 guide, which also covers both models on Mistral's API.

API usage: one client for both models

Both APIs speak the OpenAI Chat Completions format, so the official openai Python SDK works with only the base URL and model changed. Two details trip people up:

1. Thinking control. GLM-5.3-Flash only accepts thinking.type: enabled. DeepSeek accepts enabled or disabled. Both take reasoning_effort. With the OpenAI SDK, non-standard fields go in extra_body.
2. Cache usage fields. DeepSeek returns prompt_cache_hit_tokens in usage. Z.ai returns prompt_tokens_details.cached_tokens. If you log only one, your cost estimates for the other will be wrong.

"""Switch between GLM-5.3-Flash and DeepSeek-V4.1-Flash with one env var.

Usage: LLM_PROVIDER=zai ZAI_API_KEY=... python flash_client.py
       LLM_PROVIDER=deepseek DEEPSEEK_API_KEY=... python flash_client.py
"""
from __future__ import annotations

import os
from dataclasses import dataclass
from datetime import datetime, timezone
from typing import Any

from openai import OpenAI


@dataclass(frozen=True)
class Provider:
    base_url: str
    model: str
    key_env: str
    # USD per 1M tokens: (input cache miss, input cache hit, output)
    price: tuple[float, float, float]
    extra_body: dict[str, Any]


PROVIDERS: dict[str, Provider] = {
    "zai": Provider(
        base_url="https://api.z.ai/api/paas/v4/",
        model="glm-5.3-flash",
        key_env="ZAI_API_KEY",
        price=(0.15, 0.03, 0.50),
        # Thinking cannot be disabled on GLM-5.3-Flash; effort is low/high/max.
        extra_body={"thinking": {"type": "enabled"}, "reasoning_effort": "high"},
    ),
    "deepseek": Provider(
        base_url="https://api.deepseek.com",
        model="deepseek-flash",
        key_env="DEEPSEEK_API_KEY",
        price=(0.15, 0.003, 0.60),  # off-peak; peak is double
        extra_body={"thinking": {"type": "enabled"}, "reasoning_effort": "high"},
    ),
}


def deepseek_is_peak(now: datetime) -> bool:
    """Peak: 01:00-04:00 and 06:00-10:00 UTC, Monday to Friday.

    Chinese public holidays are also off-peak; this check ignores them.
    """
    utc = now.astimezone(timezone.utc)
    if utc.weekday() >= 5:
        return False
    return 1 <= utc.hour < 4 or 6 <= utc.hour < 10


def cached_tokens(usage: Any) -> int:
    # DeepSeek reports prompt_cache_hit_tokens; Z.ai reports
    # prompt_tokens_details.cached_tokens.
    hit = getattr(usage, "prompt_cache_hit_tokens", None)
    if hit is None and getattr(usage, "model_extra", None):
        hit = usage.model_extra.get("prompt_cache_hit_tokens")
    if hit is not None:
        return int(hit)
    details = getattr(usage, "prompt_tokens_details", None)
    return int(getattr(details, "cached_tokens", 0) or 0)


def cost_usd(name: str, prompt: int, cached: int, completion: int,
             now: datetime | None = None) -> float:
    miss_p, hit_p, out_p = PROVIDERS[name].price
    if name == "deepseek" and deepseek_is_peak(now or datetime.now(timezone.utc)):
        miss_p, hit_p, out_p = miss_p * 2, hit_p * 2, out_p * 2
    miss = prompt - cached
    return (miss * miss_p + cached * hit_p + completion * out_p) / 1_000_000


def main() -> None:
    name = os.environ.get("LLM_PROVIDER", "deepseek")
    p = PROVIDERS[name]
    client = OpenAI(
        api_key=os.environ[p.key_env],
        base_url=os.environ.get("LLM_BASE_URL", p.base_url),
    )
    resp = client.chat.completions.create(
        model=os.environ.get("LLM_MODEL", p.model),
        messages=[
            {"role": "system", "content": "You are a concise support bot."},
            {"role": "user", "content": "Summarise our refund policy in two lines."},
        ],
        extra_body=p.extra_body,
    )
    print(resp.choices[0].message.content)
    u = resp.usage
    if u is not None:
        hit = cached_tokens(u)
        print(f"{name}: {u.prompt_tokens} in ({hit} cached), "
              f"{u.completion_tokens} out, ${cost_usd(name, u.prompt_tokens, hit, u.completion_tokens):.6f}")


if __name__ == "__main__":
    main()

I type-checked this with mypy --strict (openai SDK 3.42.0) and ran it against a local mock server that returns each vendor's usage format. With a 3,000-token prompt (2,000 cached) and 800 output tokens it prints $0.000610 for GLM and $0.000636 for DeepSeek off-peak, which matches Scenario B divided by 1,000. I did not make paid live calls for this post, so test with your own keys before production.

LLM_MODEL lets you point at glm-5.3-flashx when you need speed (Z.ai quotes about 200 tokens per second) or at another gateway that hosts the same weights. Both models are also on Mistral's API as third-party models (zai-glm-5-3-flash and DeepSeek V4.1 Flash 1017), listed on its models page, which helps if you already have a Mistral contract.

Licence and self-hosting

Both weight repositories are tagged MIT on Hugging Face: zai-org/GLM-5.3-Flash and deepseek-ai/DeepSeek-V4.1-Flash-1017. MIT allows commercial use, fine-tuning and serving the model to customers, as long as you keep the copyright and licence notice. That is a simpler position than Mistral Large 4's licence, which needs a separate deal for model-as-a-service businesses.

Self-hosting is still a large-server job. GLM-5.3-Flash has about 321 billion parameters in its safetensors, and Z.ai lists SGLang and vLLM recipes. DeepSeek V4.1-Flash is bigger at 552B total. For most small teams the API is far cheaper than renting GPUs, and self-hosting only makes sense if data must stay on your own hardware.

Which one should you pick?

• Coding agents and long-context RAG with heavy prompt reuse: DeepSeek-V4.1-Flash, and schedule batch jobs off-peak.
• User-facing chatbots during Asian business hours: GLM-5.3-Flash, because its price does not change with the clock and its output is cheaper.
• Agents that read video, screen recordings or files: GLM-5.3-Flash, which accepts those input types.
• Workloads where you want thinking off for speed or cost: DeepSeek, since GLM-5.3-Flash cannot disable thinking. Use reasoning_effort: low on GLM instead.
• Either way: keep the provider behind an environment variable, log cached tokens from both usage formats, and rerun your cost check monthly. DeepSeek says its prices may change, and Z.ai calls free cache storage "limited-time".

If you are comparing more than these two, my LLM API pricing guide uses the same cost-per-task method across the main providers, and the Kimi K3.1 Pro API guide covers another open-weight option.