AIモデルインテリジェンス

Ling-2.6-flash

openrouter/ling-2-6-flash

提供: openrouter · ファミリー: ling · リリース 2026-04-21

$0.010
入力 / 100万トークン
$0.030
出力 / 100万トークン
262K
コンテキスト長
33K
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.

Coding69
  • Tool calling40/40
  • Structured output20/20
  • Reasoning0/10
  • Context window (100K → 1M)8/20
  • Provider availability1/10
Agents76
  • Tool calling35/35
  • Structured output25/25
  • Reasoning0/15
  • Output token limit15/15
  • Provider availability1/10
JSON / structured output100
  • Structured output / JSON mode50/50
  • Tool calling20/20
  • Temperature control10/10
  • Price-friendly for high-volume20/20
Cost efficiency100
  • Headline price (log-scaled)95/95
  • Has prompt-cache pricing5/5
Long context61
  • Context window (100K → 2M)51/90
  • Has published price for full window10/10
Production-readiness65
  • Number of independent providers5/40
  • Has published per-token price20/20
  • Context window ≥ 8K15/15
  • 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
$0.06
< $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
$0.13
< $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
$0.03
< $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
$0.11
< $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
$0.14
< $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): openrouter · inclusionai/ling-2.6-flash

$0.010
入力
$0.030
出力
$0.002
キャッシュ読み取り

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
OpenRouter
openrouter
inclusionai/ling-2.6-flash$0.010$0.030262K2026-04-21

Frequently asked questions

How much does Ling-2.6-flash cost?

Ling-2.6-flash costs $0.010 per 1M input tokens and $0.030 per 1M output tokens, sourced from openrouter. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of Ling-2.6-flash?

Ling-2.6-flash has a context window of 262K tokens, with a max output of 33K tokens per reply. This is the total combined size of prompt + completion.

Does Ling-2.6-flash support tool calling?

Yes. Ling-2.6-flash supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.

Does Ling-2.6-flash support structured output / JSON mode?

Yes. Ling-2.6-flash supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.

Can Ling-2.6-flash accept image input?

No. Ling-2.6-flash only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is Ling-2.6-flash open-weight?

No. Ling-2.6-flash is a proprietary model — only openrouter (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.

What are the best alternatives to Ling-2.6-flash?

If Ling-2.6-flash doesn't fit, consider INTELLECT-3, LFM2-24B-A2B, LFM2.5-1.2B-Thinking (free). 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.

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.