AIモデルインテリジェンス

Synth 2.5 Flash Preview

nano-gpt/synth-2-5-flash

提供: nano-gpt · リリース 2026-09-05

$0.030
入力 / 100万トークン
$0.100
出力 / 100万トークン
30K
コンテキスト長
30K
最大出力

Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.

機能一覧

✗ ツール呼び出し✗ 推論✗ 構造化出力✗ 添付✗ オープンウェイト? 温度制御
モダリティ: 入力 text · 出力 text

Model fit scores

0–100 · higher is better

These scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.

Coding1
  • Tool calling0/40
  • Structured output0/20
  • Reasoning0/10
  • Context window (100K → 1M)0/20
  • Provider availability1/10
Agents16
  • Tool calling0/35
  • Structured output0/25
  • Reasoning0/15
  • Output token limit15/15
  • Provider availability1/10
JSON / structured output20
  • Structured output / JSON mode0/50
  • Tool calling0/20
  • Temperature control0/10
  • Price-friendly for high-volume20/20
Cost efficiency84
  • Headline price (log-scaled)84/95
  • Has prompt-cache pricing0/5
Long context0
  • Context ≥ 100K0/100
Production-readiness58
  • Number of independent providers5/40
  • Has published per-token price20/20
  • Context window ≥ 8K8/15
  • No data inconsistencies across providers10/10
  • Official model (not derivative)15/15

Cost Efficiency Index

Open full calculator →

Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.

ScenarioCostAssumption
RAG answer
per 1,000 RAG answers
$0.20
< $0.01 per request
5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot.
Support ticket triage
per 10,000 tickets
$0.40
< $0.01 per request
1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support.
Data extraction
per 1,000 documents
$0.11
< $0.01 per request
2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines.
Code review
per 1,000 PRs
$0.34
< $0.01 per request
8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads.
Agent step
per 1,000 steps
$0.42
< $0.01 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

料金詳細

推奨料金 (提供元): nano-gpt · synth-2.5-flash

$0.030
入力
$0.100
出力

1 か所で利用可能

プロバイダープロバイダーモデルID入力 / 1M出力 / 1Mコンテキストリリース日
NanoGPT
nano-gpt
synth-2.5-flash$0.030$0.10030K2026-09-05

Frequently asked questions

How much does Synth 2.5 Flash Preview cost?

Synth 2.5 Flash Preview costs $0.030 per 1M input tokens and $0.100 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of Synth 2.5 Flash Preview?

Synth 2.5 Flash Preview has a context window of 30K tokens, with a max output of 30K tokens per reply. This is the total combined size of prompt + completion.

Does Synth 2.5 Flash Preview support tool calling?

No. Synth 2.5 Flash Preview does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.

Does Synth 2.5 Flash Preview support structured output / JSON mode?

No. Synth 2.5 Flash Preview does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.

Can Synth 2.5 Flash Preview accept image input?

No. Synth 2.5 Flash Preview only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is Synth 2.5 Flash Preview open-weight?

No. Synth 2.5 Flash Preview is a proprietary model — only nano-gpt (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.

What are the best alternatives to Synth 2.5 Flash Preview?

If Synth 2.5 Flash Preview doesn't fit, consider Moonlight Dusk, Shadow Siren, Ernie 5.0 Thinking Preview. Each one targets the same use case — see the Related links below for direct head-to-head pages.

Where does this data come from?

All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.

More nano-gpt models

最終更新:

Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.