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DeepSeek V4 Pro (0813) vs GLM-5.2

vs

Which one, when — curated verdict, not a benchmark table

Both share a 1,000,000-token context and a text-in, text-out profile with chat, code, reasoning and tools, so the split is really about output length and price: deepseek-v4-pro-0813 costs about 1.7x more on input ($1.32 vs $0.77) and roughly 1.6x more on output ($3.96 vs $2.42), but allows 393216 output tokens against 131072. Pick deepseek-v4-pro-0813 when a single response must run very long, or when heavy cache reuse favours its $0.132 cache-read rate over $0.143. Pick glm-5.2 for cheaper high-volume work, or when you want to disable thinking per request.

Pricing

DeepSeek V4 Pro (0813) GLM-5.2 Δ
Input / 1M tokens $1.32 $0.77 1.7×
Output / 1M tokens $3.96 $2.42 1.6×
Cache read / 1M tokens $0.132 $0.143 0.92×
Cache write no separate charge

Rates from the live catalog at build time; each model page carries the current card.

Where they sit — input price per 1M tokens across all 63 chat models on this billing unit (log scale)

DeepSeek V4 Pro (0813) · $1.32 GLM-5.2 · $0.77
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

Capabilities

DeepSeek V4 Pro (0813) GLM-5.2
Tool use yes yes
Thinking control always on configurable
Structured output yes yes
Prompt caching implicit (automatic) implicit (automatic)
Cache lifetime no fixed TTL (evicted when unused) not published
Minimum cached prefix not published not published

Specs

DeepSeek V4 Pro (0813) GLM-5.2
Input modalities text text
Output modalities text text
Released 2026-08-13 2026-06-16
Context window 1M 1M
Max output 393K 131K
Thinking parameter reasoning_effort
  • thinking.type
  • reasoning_effort
Accepted values
reasoning_effort
  • the model card documents low
  • high
  • max
thinking.type
  • enabled
  • disabled
reasoning_effort
  • none
  • minimal
  • low
  • medium
  • high
  • xhigh
  • max (none and minimal skip thinking, low and medium map to high, xhigh maps to max)
Default enabled, with reasoning_effort at max: the only GLM with an effort dial, and it defaults to the top of it

Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: DeepSeek V4 Pro (0813) · GLM-5.2

One prompt, both models — measured through the gateway

PROMPT Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. CHECK exactly 3 sentences

DeepSeek V4 Pro (0813) passed · 3 sentences

Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.

out 308 tok (+226 thinking) latency 5.1 s

GLM-5.2 passed · 3 sentences

Enabling reasoning means the LLM now generates thousands of invisible "thinking" tokens to systematically work through complex problems before producing a final answer. Because our cloud providers bill for these internal processing steps at the same rate as standard output, our billable token volume per query has doubled. While this increases our direct API costs, it drastically reduces error rates and manual review labor, ultimately lowering our total cost per resolved transaction.

out 1223 tok (+1138 thinking) latency 17.1 s

Instruction following (exactly three sentences — countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.

PROMPT This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. CHECK fix passes tests

DeepSeek V4 Pro (0813) passed · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.

out 1315 tok (+1130 thinking) latency 16.9 s

GLM-5.2 missed · 1/8 cases (fails [1])

```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 […]

out 4097 tok (+4036 thinking) latency 58.4 s

Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.

PROMPT Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. CHECK valid JSON, schema exact

DeepSeek V4 Pro (0813) passed · 5/5 fields, guidance null

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}

out 2155 tok (+2121 thinking) latency 26.0 s

GLM-5.2 passed · 5/5 fields, guidance "withheld"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld" } ```

out 1947 tok (+1893 thinking) latency 30.9 s

Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.

PROMPT Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. CHECK 120 words, 0 banned words

DeepSeek V4 Pro (0813) passed · 121 words, 0 banned, 1 question

Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.

out 2845 tok (+2694 thinking) latency 25.5 s

GLM-5.2 passed · 120 words, 0 banned, 1 question

We are introducing Caching for our API Gateway, the smartest way to optimize your workflows. Why pay for the exact same response twice? Now, you can automatically store and reuse prompt results across multiple AI providers, drastically reducing latency and overall operational costs. If a user submits a duplicate query, the gateway serves the cached answer instantly, regardless of whether you route to OpenAI, Anthropic, or others. This directly translates to faster applications and significantly lower monthly API bills. You can easily configure your specific caching rules within the developer dashboard and watch your efficiency soar. Stop wasting your valuable tokens on completely redundant computations. Upgrade to the latest gateway version today and experience the future of intelligent prompt management.

out 11125 tok (+10984 thinking) latency 114.8 s

Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.

Switch between them with one line

Both ids are in every tab below — the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="deepseek-v4-pro-0813",
    # model="glm-5.2",  # uncomment this line, comment the one above
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

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FAQ

Which is cheaper, DeepSeek V4 Pro (0813) or GLM-5.2?

GLM-5.2 is cheaper on input / 1m tokens ($0.77 vs $1.32, 1.7× apart). Other rows may point the other way — the table above carries the full card, and real cost depends on your mix.

Can I A/B test DeepSeek V4 Pro (0813) against GLM-5.2 without two integrations?

Yes. Both are served through the same OpenAI-compatible endpoint with one API key — switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.

Do DeepSeek V4 Pro (0813) and GLM-5.2 support prompt caching?

Yes — both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.

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