New Sign up free, 10 calls on us. Up to $1, no card needed.

DeepSeek V4.1 Flash vs GPT-6 Sol

vs

Which one, when

Both take text and image in and return text, carry the same chat, vision, code, reasoning and tools flags, and sit at near-identical context (1,000,000 for deepseek-v4.1-flash vs 1,050,000 for gpt-6-sol). Pick deepseek-v4.1-flash for volume and long generations: roughly 6.7x cheaper input ($0.3 vs $2), about 8.3x cheaper output ($1.2 vs $10), cache reads at $0.03 vs $0.2, and 393216 max output tokens against 128000. Choose gpt-6-sol when you want reasoning that can be turned off per request or a stated 2026-04 knowledge cutoff to plan around.

Benchmarks

Above averageNo peer higherDeepSeek V4.1 Flash14 / 194 / 19GPT-6 Solonly 4 comparable
DeepSeek V4.1 Flash GPT-6 Sol other models measured peer average no peer scored higher
DeepSWE 1.1
no peer scored higher 74.2%
68.8%
OSWorld 2.0 offline set, partial
N/A
60.5%
Cybergym
no peer scored higher 88.1%
N/A
GPQA Diamond
90.9%
N/A
Agents' Last Exam
31.8%
56.4%
Chartography with tools
78.9%
N/A

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

Pricing

DeepSeek V4.1 Flash GPT-6 Sol Δ
Input / 1M tokens $0.3 $2 0.15×
Output / 1M tokens $1.2 $10 0.12×
Cache read / 1M tokens $0.03 $0.2 0.15×
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)

Capabilities

DeepSeek V4.1 Flash GPT-6 Sol
Tool use yes yes
Thinking control yes - vendor dial not published configurable
Structured output - 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 V4.1 Flash GPT-6 Sol
Input modalities text image text image
Output modalities text text
Released 2026-09-10 2026-09-22
Knowledge cutoff - 2026-04
Context window 1M 1.1M
Max output 393K 128K
Thinking parameter - reasoning.effort
Accepted values -
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 V4.1 Flash · GPT-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 V4.1 Flash passed · 3 sentences

Enabling reasoning changed the unit economics: the model now spends extra billable tokens internally to “think” before producing the answer, and those reasoning tokens are charged like output tokens even though users never see them. That added token generation—plus longer contexts and occasional retries on harder prompts—roughly doubled our cost per request. We can control this by capping reasoning depth, routing only complex queries to reasoning models, and caching common prompts to bring the bill back down.

out 423 tok (+327 thinking) latency 14.6 s

GPT-6 Sol passed · 3 sentences

Enabling reasoning made the model generate extra internal tokens to work through each request, and those tokens are billed even though users don’t see them. If request volume stayed roughly the same, more billed tokens per request could explain why the bill doubled. We should check the usage breakdown to confirm, then lower reasoning effort or limit it to tasks that need it.

out 188 tok (+111 thinking) latency 5.0 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 V4.1 Flash 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` is counting equal adjacent pairs, not elements in the current run: every time `xs[i] == xs[i - 1]`, the run length increases by one element, but the first element of the run was never counted, so all runs are undercounted by one. Also, `best` starts at `0`, so single-element inputs return `0` instead of `1`, and a one-element run ending at the last index is missed entirely.

out 918 tok (+733 thinking) latency 11.4 s

GPT-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 `cur` counted matching *adjacent pairs*, not items, so every run was one too short—including a single-element input, which returned 0. Starting each run at 1 counts its first item, and updating `best` on every iteration ensures a run that reaches the last index is included.

out 206 tok (+59 thinking) latency 5.9 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 V4.1 Flash passed · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":"withheld pending the Q4 close"}

out 1707 tok (+1667 thinking) latency 15.1 s

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

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

out 227 tok (+185 thinking) latency 5.2 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 V4.1 Flash passed · 116 words, 0 banned, 1 question

Today we're launching Cross-Provider Prompt Cache for our API gateway. It stores identical prompt requests and their responses across supported model providers, then serves cached results when a match is found. Teams can cut duplicate inference costs, reduce latency, and keep behavior consistent during provider failover. The cache works with configurable TTLs, per-route rules, and cache-key controls, so you decide what is reusable and what must stay fresh. Does your application send the same prompts to multiple providers? Now your gateway can answer many of those calls without another upstream request. Existing observability dashboards show hit rates, saved tokens, and estimated spend reduction. Enable it in the gateway console, set your policy, and start caching today.

out 917 tok (+770 thinking) latency 9.0 s

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

Today, we’re introducing cross-provider prompt caching for our API gateway, helping teams reuse repeated prompt content when routing requests among supported AI providers. The gateway identifies eligible prompt prefixes, stores cache references, and applies them to subsequent requests where provider capabilities allow. Why send the same context again if it can be reused? With fewer duplicate input tokens, applications can reduce costs and improve latency without changing how developers call the gateway. Configure caching by route, monitor hit rates and savings in your dashboard, and keep existing provider fallback rules in place. Availability and cache behavior vary by provider, so the gateway reports eligibility and usage for each request. Get started today with our documentation and test your existing workloads.

out 586 tok (+443 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-v4.1-flash",
    # model="gpt-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)

Get your API key →

FAQ

Which is cheaper, DeepSeek V4.1 Flash or GPT-6 Sol?

DeepSeek V4.1 Flash is cheaper on input / 1m tokens ($0.3 vs $2, 6.7× 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 V4.1 Flash against GPT-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 V4.1 Flash and GPT-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.

Related comparisons

From our measured studies