GLM vs DeepSeek vs MiniMax: which model to choose

All three are open-weight models available through the same OpenAI-compatible API, with the same key. They differ in context size, output limit and price — and for most projects that is what decides. Limits below are as of October 20, 2026; prices and costs in the tables further down are live and always show current rates.

At a glance#

Limits come from GET https://api.cheaptokens.dev/v1/models — the same list your tools see.

Current prices (live)#

This table updates automatically: rates per million tokens, input and output separately — the same data as on the pricing page. OpenRouter figures are the list prices of the same models on openrouter.ai that we track for comparison.

Cost of a typical request#

Conditions: one request with 20,000 input tokens and 2,000 output tokens — a mid-sized coding question with a few files in context. The table is calculated from current prices and recalculates when they change; the OpenRouter column shows the same workload at the reference prices we track.

How to choose#

These are starting points based on the limits and prices above — not benchmark results: we have not run quality tests of the three models.

• Long outputs — whole files, long reports, big refactors in one answer: DeepSeek-V4-Flash, the only one of the three with a 32K output limit.
• Lowest cost per request: see the table above — it is recalculated whenever prices change.
• Hard reasoning — multi-step changes, tricky bugs: GLM-5.3-Flash. It reasons before answering; the reasoning is generated text too, so it adds output tokens and time.
• Agents and automation: MiniMax-M2.7. Its context is the smallest of the three (200K), so trim long histories.
• Very long context — a large codebase or document set: GLM-5.3-Flash (390K) or DeepSeek-V4-Flash (380K).

The most reliable test is your own. A simple method: write down the model IDs and the date; take 10–20 real tasks from your work; run each through all three models with the same prompt; score correctness, how well instructions were followed, answer length and response time; read the token counts from usage and multiply them by the current rates.

Switching models#

The API, base URL and key are the same for all models — only the model field changes:

import os
from openai import OpenAI

client = OpenAI(base_url="https://api.cheaptokens.dev/v1", api_key=os.environ["CHEAPTOKENS_API_KEY"])

for model in ["zai-org/GLM-5.3-Flash", "deepseek-ai/DeepSeek-V4-Flash-0731", "MiniMaxAI/MiniMax-M2.7"]:
    resp = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Summarize the CAP theorem in two sentences."}],
    )
    print(model, resp.usage.prompt_tokens, resp.usage.completion_tokens)
    print(resp.choices[0].message.content, "\n")

The catalog changes over time: new models are added and some can be temporarily unavailable. Before hard-coding an ID, check GET /v1/models.