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GPT-6 Luna vs GPT-6 Sol

vs

Which one, when

gpt-6-luna and gpt-6-sol are the same shape on paper: both carry a 1050000-token context, 128000-token max output, text-and-image input with text output, the same chat/vision/code/tools/reasoning flags, optional thinking, and a 2026-09-22 release. The separation is price: gpt-6-sol costs 20x more on every line ($2 vs $0.1 input, $10 vs $0.5 output, $0.2 vs $0.01 cache reads), so make gpt-6-luna the default for high-volume and long-context work. Reach for gpt-6-sol only when testing on your own prompts justifies the 20x; gpt-6-luna even carries the later knowledge cutoff, 2026-05 against 2026-04.

Benchmarks

Above averageGPT-6 Lunaonly 1 comparableGPT-6 Solonly 4 comparable
GPT-6 Luna GPT-6 Sol other models measured peer average no peer scored higher
DeepSWE 1.1
66.6%
68.8%
OSWorld 2.0 offline set, partial
N/A
60.5%
Agents' Last Exam
N/A
56.4%

Vendor-published: Alibaba (Qwen) Anthropic DeepSeek Google OpenAI Z.ai

Pricing

GPT-6 Luna GPT-6 Sol Δ
Input / 1M tokens $0.1 $2 0.05×
Output / 1M tokens $0.5 $10 0.05×
Cache read / 1M tokens $0.01 $0.2 0.05×
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)

GPT-6 Luna · $0.1 GPT-6 Sol · $2
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

Capabilities

GPT-6 Luna GPT-6 Sol
Tool use yes yes
Thinking control configurable configurable
Structured output yes yes
Prompt caching implicit (automatic) implicit (automatic)
Cache lifetime 5-10m, up to 1h 5-10m, up to 1h
Minimum cached prefix 1024 tokens 1024 tokens

Specs

GPT-6 Luna GPT-6 Sol
Input modalities text image text image
Output modalities text text
Released 2026-09-22 2026-09-22
Knowledge cutoff 2026-05 2026-04
Context window 1.1M 1.1M
Max output 128K 128K
Thinking parameter reasoning.effort reasoning.effort
Accepted values
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
Default medium medium

Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: GPT-6 Luna · 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

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

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

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

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

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

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

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

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="gpt-6-luna",
    # 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)

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FAQ

Which is cheaper, GPT-6 Luna or GPT-6 Sol?

GPT-6 Luna is cheaper on input / 1m tokens ($0.1 vs $2, 20× 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 GPT-6 Luna 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 GPT-6 Luna 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.

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