DeepSeek V4 vs GLM‑5.1: Which AI Coding Model Offers the Best Cost‑Performance?

1. Pricing Overview

DeepSeek V4 uses a cache‑hit / miss pricing model. For V4‑Flash a cache hit costs ¥0.02 per million tokens while a miss costs ¥1, a 50× difference; for V4‑Pro the special price is ¥0.025 hit vs ¥3 miss, a 120× gap. The actual cost therefore depends on three factors: cache‑hit rate, uncached input tokens, and output tokens.

Cache hits occur when the same system prompt is reused across large‑scale requests, allowing the KV cache to skip the expensive pre‑fill stage. Fixed prompts, tool definitions, and high context‑reuse tasks (e.g., batch code review, document processing) benefit most from this cache advantage.

Compared with OpenAI and Anthropic, DeepSeek enables context caching by default without manual breakpoints.

2. GLM Coding Plan Subscription

GLM‑5.1 offers a subscription‑based Coding Plan: Lite ¥49, Pro ¥149, Max ¥469 per month. Within the quota there are no extra token charges, but acquiring a new quota often requires a competitive reservation process.

3. Theoretical Cost Calculation

Assuming a conservative 85% cache‑hit rate and an input‑to‑output ratio of 3:1:

V4‑Flash mixed price ≈ ¥0.63 per million tokens.

V4‑Pro special price ≈ ¥1.85 per million tokens (with 85% hit).

Monthly cost estimates (based on the assumed hit rate) show that GLM’s subscription appears cheaper for billions of tokens, but the estimate relies on a high output proportion and a fixed hit rate.

4. Real Billing Data

First dataset (May 5):

Metric                Value
----------------------------
Total tokens          98,955,701
Input (cache hit)      98,522,368
Input (miss)          224,575
Output                208,758
Actual bill           ¥4.43 (V4‑Flash ¥0.04, V4‑Pro ¥4.38)
Cache‑hit rate         99.77%
Output share          0.21%

The bill far exceeds the simple total‑tokens‑×‑average‑price estimate.

Second dataset: 17 million tokens, ~95% cache‑hit rate, actual bill just over ¥1.

These data illustrate that DeepSeek’s cost cannot be judged solely by total token count; cache efficiency is decisive.

5. When Each Model Excels

Fixed‑template, high‑reuse tasks (batch code review, document processing, fixed Agent loops): DeepSeek becomes cheaper once cache‑hit rates exceed 95%, potentially reducing cost to a few yuan per 100 million tokens.

Everyday coding: DeepSeek remains competitive if prompts and tool definitions stay stable, especially with V4‑Flash.

Long, unpredictable tasks or heavy Agent loops: GLM’s fixed monthly fee provides cost certainty, making it preferable for heavy users who cannot guarantee high cache‑hit rates.

Production APIs and automated batch jobs: DeepSeek’s pay‑as‑you‑go model with low cache‑hit cost is advantageous, whereas GLM’s subscription is more suited to personal developer workflows.

6. Money‑Saving Tips

Prefer the “high” or “max” model tier on DeepSeek; “medium” is rarely needed.

Use /compact to compress context for ongoing tasks and /clear to reset history for new tasks.

Place invariant content (system prompt, tool definitions) at the beginning of the prompt and variable content later to maximize cache hits.

Load full Skill definitions lazily: keep only names and brief descriptions in the context, loading the full text only when needed.

The core of AI‑coding cost savings lies in avoiding unnecessary token consumption rather than chasing marginal price differences.

Conclusion

GLM Coding Plan offers a fixed monthly fee and cost certainty, ideal for daily coding, long tasks, and unpredictable Agent loops. DeepSeek’s API provides extremely low cost when cache hits are high, making it the better choice for fixed‑template, batch, or production automation scenarios. The key decision factor is whether your workload can achieve a high cache‑hit rate and keep output tokens low.