GLM 5.2 vs GPT 5.6 vs DeepSeek V4 Pro: An Honest Buying Guide

The short answer

If your first decision is cost and API simplicity, start with GLM 5.2. OneMux lists it at $0.45 per 1M input tokens and $1.60 per 1M output tokens, placing it below DeepSeek V4 Pro and the listed GPT 5.6 Terra and Sol variants on price.

Choose DeepSeek V4 Pro when its behavior on your own reasoning, extraction, or coding test set justifies the higher rate. Choose GPT 5.6 when you specifically want to evaluate OpenAI’s available variants, but compare the variants individually rather than treating GPT 5.6 as one price or one operating profile.

That is the honest limit of a catalogue-based comparison: published prices show procurement tradeoffs, not accuracy, latency, or reliability. The Reddit post supplied for this article is the source discussion. Because no source summary or verified benchmark results were provided, this article does not attribute performance claims to that post.

Published API pricing: GLM 5.2 vs GPT 5.6 vs DeepSeek V4 Pro

The following rates are the OneMux catalogue prices supplied for this comparison. They are per 1M tokens, not per request.

For a workload with equal input and output volume, GLM 5.2 costs less than DeepSeek V4 Pro on both token categories. GPT 5.6 Luna is a special case: its listed price is lower than GLM 5.2, while Terra and Sol are higher. Do not infer that the cheapest model is automatically the best value. A model that needs retries, longer prompts, or human correction can erase a nominal token-price advantage.

You can verify the current GLM 5.2 model page, review DeepSeek V4 Pro, and inspect GPT 5.6 Terra before making a routing decision.

Where GLM 5.2 makes the strongest case

GLM 5.2 is the most straightforward default when three conditions are true:

1. Your application needs a general-purpose model rather than a specialised image workflow.
2. Token cost matters, especially for high-volume prompts or customer-facing automation.
3. You want to test the model through a unified, OpenAI-compatible API instead of rebuilding provider-specific integrations.

The supplied OneMux catalogue describes GLM 5.2 as available through unified model routing. It does not provide verified claims about benchmark scores, context length, tool use, latency, or domain-specific accuracy. That means buyers should evaluate it with their own prompts instead of relying on a generic ranking.

For developers, a sensible GLM 5.2 pilot includes structured JSON extraction, code edits, multilingual inputs, refusal handling, and long-but-realistic support tickets. For operators, measure cost per completed task rather than cost per token. For marketers, test whether the model follows brand constraints across revisions. For support teams, check escalation accuracy and whether the output preserves names, numbers, and policy language.

GPT 5.6: compare the variants, not the label

The catalogue lists GPT 5.6 Luna, Terra, and Sol separately, with substantially different prices. That matters for architecture. A single environment might use a lower-cost variant for classification or drafting and reserve a higher-priced variant for difficult cases.

Do not describe GPT 5.6 as universally better or worse than GLM 5.2 from the pricing table alone. The available data supports only a cost comparison. Your test should include:

• Exact-match or schema-validity rate for structured tasks.
• Useful completion rate for coding and analysis tasks.
• Median and tail latency under realistic concurrency.
• Retry frequency and average tokens per successful task.
• Human correction time for marketing and support outputs.

This is also where routing becomes more useful than a one-model decision. OneMux provides access to models through one OpenAI-compatible API, with model access, routing, keys, spend visibility, and credit top-ups in the product scope described here. That lets a team keep its application integration stable while comparing model IDs and costs.

DeepSeek V4 Pro: the middle-cost alternative

DeepSeek V4 Pro sits between GLM 5.2 and the two higher-priced GPT 5.6 variants in the supplied rates. Its listed price is $0.70 per 1M input tokens and $2.10 per 1M output tokens. That makes it a reasonable candidate for an A/B test when GLM 5.2 is attractive on cost but your evaluation set suggests another model may complete more tasks without intervention.

The important word is candidate. No verified benchmark or capability summary beyond catalogue availability was supplied. Treat DeepSeek V4 Pro as a model to measure, not a guaranteed reasoning or coding winner.

A useful test is to send the same anonymised task set to GLM 5.2 and DeepSeek V4 Pro, preserve generation settings, and score outputs blind. Include failed cases in the report. A model that wins only on easy prompts may not be the right production choice; a model that costs more but reduces review work may be.

Where does Qwen fit?

Qwen belongs in a serious GPT-versus-DeepSeek comparison because teams may already use it for multilingual, coding, or regional deployment requirements. However, the supplied OneMux catalogue does not list a Qwen model, price, or verified capability summary. It would be irresponsible to invent a Qwen ranking or present it as available through OneMux based on the information provided.

The practical approach is to add Qwen only when you have a specific model identifier and a verified access path. Then run the same evaluation used for GLM 5.2, GPT 5.6, and DeepSeek V4 Pro. Keep provider availability, data handling requirements, token pricing, and operational controls in separate columns so a performance impression does not become a procurement assumption.

A production test plan for founders and operators

Start with 50 to 200 representative tasks, not synthetic prompts alone. Split them by use case: support, extraction, coding, research, content, and internal operations. Record the input and output token counts, model ID, response status, latency, schema validity, reviewer score, and retry count.

Then calculate cost per accepted result. This is more useful than comparing advertised input rates in isolation:

accepted_result_cost = total_token_cost / accepted_results

Use separate traffic pools for experiments and production. Set a budget threshold, log provider and model identifiers, redact sensitive content, and define a fallback policy for timeouts or invalid structured output. Before implementation, consult the OneMux quickstart and the broader OneMux API documentation. For current account and usage details, check OneMux pricing.

Verdict: start with GLM 5.2, then earn the switch

GLM 5.2 is the sensible first test when you want a low published token price and a unified API route. DeepSeek V4 Pro is the nearest listed price comparison, while GPT 5.6 requires variant-by-variant evaluation because Luna, Terra, and Sol have very different rates. Qwen should remain a separately verified candidate rather than an unsupported conclusion.

The decision should ultimately be based on accepted results per dollar for your workload. Use OneMux to keep model access and routing practical, but let your own evaluation set—not a headline comparison—decide which model receives production traffic.