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DeepSeek V4 Pro (0813) vs GPT-6 Luna

vs

Which one, when

Both cover chat, code, reasoning and tools across roughly a million tokens of context (1,000,000 for deepseek-v4-pro-0813, 1,050,000 for gpt-6-luna), so the real split is price and shape of the job. Pick gpt-6-luna for cheaper, higher-volume work, $0.1 input and $0.5 output versus $1.32 and $3.96, making deepseek-v4-pro-0813 13.2x the input and 7.92x the output cost, and for image input plus the option to turn thinking off. Pick deepseek-v4-pro-0813 when you need very long single responses, since its 393216 max output tokens is about three times Luna's 128000.

Benchmarks

Above averageNo peer higherDeepSeek V4 Pro (0813)13 / 211 / 21GPT-6 Lunaonly 1 comparable
DeepSeek V4 Pro (0813) GPT-6 Luna other models measured peer average no peer scored higher
DeepSWE 1.1
62.7%
66.6%
Cybergym
83.3%
N/A
GDPval-AA v2 Elo · 1508-1769 per Z.ai · 2026-09-04
1590
N/A
GPQA Diamond
92.4%
N/A
Agents' Last Exam
25.7%
N/A

Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

Pricing

DeepSeek V4 Pro (0813) GPT-6 Luna Δ
Input / 1M tokens $1.32 $0.1 13×
Output / 1M tokens $3.96 $0.5 7.9×
Cache read / 1M tokens $0.132 $0.01 13×
Cache write no separate charge 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 74 chat models on this billing unit (log scale)

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

Capabilities

DeepSeek V4 Pro (0813) GPT-6 Luna
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) 5-10m, up to 1h
Minimum cached prefix not published 1024 tokens

Specs

DeepSeek V4 Pro (0813) GPT-6 Luna
Input modalities text text image
Output modalities text text
Released 2026-08-13 2026-09-22
Knowledge cutoff - 2026-05
Context window 1M 1.1M
Max output 393K 128K
Thinking parameter reasoning_effort reasoning.effort
Accepted values
reasoning_effort
  • the model card documents low
  • high
  • max
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
Default - medium

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) · GPT-6 Luna

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

GPT-6 Luna passed · 3 sentences

Enabling reasoning typically makes the model process more information and generate more internal tokens before producing its answer. Because usage is billed by tokens—and reasoning may also route requests to a more expensive model—the cost per request can rise sharply. If request volume stayed similar, that higher cost per request could explain why the bill doubled.

out 117 tok (+48 thinking) latency 3.4 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

GPT-6 Luna passed · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = 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 original code counted matching *adjacent pairs* rather than items, so a run of length `n` was reported as `n - 1`, and a single-item input returned `0`. This version counts the current item as part of each run and updates `best` at every index, including the last; it returns `0` for an empty input.

out 514 tok (+356 thinking) latency 6.9 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

GPT-6 Luna passed · 5/5 fields, guidance "Withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":null,"guidance":"Withheld pending the Q4 close"}

out 161 tok (+119 thinking) latency 21.7 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

GPT-6 Luna passed · 120 words, 0 banned, 1 question

Introducing Prompt Cache, a new API gateway feature that recognizes repeat prompt prefixes and reuses provider-side cached context across supported models. Teams can route requests to different AI providers while preserving eligible cache hits, reducing redundant input processing and helping lower latency and token costs. Configure cache policies in one place, monitor hit rates by provider, and keep existing client integrations unchanged. The gateway applies provider-specific rules automatically, so developers do not need to build separate caching logic for each endpoint. Which workflows could benefit from faster responses and predictable spend? Prompt Cache is available today in preview for eligible accounts, with usage details, supported providers, and setup guidance in the dashboard. Start with one route, compare results, then expand.

out 959 tok (+813 thinking) latency 14.6 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="gpt-6-luna",  # 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 GPT-6 Luna?

GPT-6 Luna is cheaper on input / 1m tokens ($0.1 vs $1.32, 13× 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 GPT-6 Luna 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 GPT-6 Luna 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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