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DeepSeek V4 Pro (0813) vs GPT-5.6 Sol

vs

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

Both are text-in reasoning models with roughly a million tokens of context (1,000,000 for deepseek-v4-pro-0813 versus 1,050,000 for gpt-5.6-sol), so the split is cost and I/O shape: DeepSeek charges $1.32 input and $3.96 output against $5 and $30, making gpt-5.6-sol about 3.8x the input price and about 7.6x the output price, and it also allows 393216 output tokens versus 128000. Pick deepseek-v4-pro-0813 for long text generation at volume; pick gpt-5.6-sol when you need image input or the option to disable thinking, neither of which DeepSeek's fact sheet lists.

Pricing

DeepSeek V4 Pro (0813) GPT-5.6 Sol Δ
Input / 1M tokens $1.32 $5 0.26×
Output / 1M tokens $3.96 $30 0.13×
Cache read / 1M tokens $0.132 $0.5 0.26×
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 63 chat models on this billing unit (log scale)

Capabilities

DeepSeek V4 Pro (0813) GPT-5.6 Sol
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-5.6 Sol
Input modalities text text image
Output modalities text text
Released 2026-08-13 2026-07-09
Knowledge cutoff 2026-02
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-5.6 Sol

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-5.6 Sol passed · 3 sentences

Enabling reasoning makes the model generate additional internal tokens before producing an answer, so each request consumes more billable compute. Reasoning may also increase response length, latency, tool calls, and retries, further raising per-request cost. In short, usage volume may be unchanged, but the cost per task increased enough to double the bill.

out 111 tok (+34 thinking) latency 2.3 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-5.6 Sol 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 function counted equal *adjacencies* rather than items, so a run of length one produced `0` and every longer run was undercounted by one. Initializing `cur` and `best` to `1` for nonempty input makes them represent item counts, resetting `cur` to `1` starts a new run at the current item, and updating `best` on every iteration ensures a run reaching the final index is included.

out 316 tok (+135 thinking) latency 5.2 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-5.6 Sol passed · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":"11+","guidance":"withheld pending the Q4 close"}

out 194 tok (+147 thinking) latency 3.2 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-5.6 Sol passed · 120 words, 0 banned, 1 question

Today, we’re introducing Provider Prompt Cache, a new API gateway feature that reuses prompt prefixes across supported AI providers, reducing latency, token costs, and duplicated processing. Teams can define cache policies once, route requests dynamically, and preserve provider flexibility without rewriting application logic. Switching models during testing or failover? The gateway identifies eligible prompt segments, applies provider-specific caching controls, and reports hits, misses, savings, and expiration details through unified logs and metrics. Configurable TTLs, tenant isolation, encryption, and cache-bypass options help teams balance performance, privacy, and freshness for every workload. Provider Prompt Cache is available today in beta through the dashboard and API, with SDK examples and migration guidance included. […]

out 733 tok (+564 thinking) latency 7.7 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-5.6-sol",  # 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-5.6 Sol?

DeepSeek V4 Pro (0813) is cheaper on input / 1m tokens ($1.32 vs $5, 3.8× 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-5.6 Sol 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-5.6 Sol 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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