Skip to content
llmwaves

Ling 3.0 Tiny (free)

InclusionAI · released Aug 6, 2026

ProprietaryReasoningTool use

Ling 3.0 Tiny is a mixture-of-experts model from InclusionAI, with 1.3B active parameters out of 7.9B total. It is designed for responsive agents, instruction following, and multi-turn conversations, with switchable...

Specification

Context window
262K
Max output
33K
Knowledge cutoff
Not stated
Parameters
Undisclosed
Licence
Proprietary
Serving providers
1
Moderated
No
Uptime
99.0%

Intelligence

24.3

67th percentile

Coding

26.5

Coding Index

Agentic

Agentic Index

Output speed

65 t/s

Median across providers

Latency

1.92s

Time to first token

Cost per task

Free

Estimated

Benchmarks

Where the score comes from

The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.

Evaluation scores

Percentage correct · higher is better

  • GPQA Diamond
    73.4%
  • AA-LCR (long context)
    58.7%
  • SciCode
    24.2%
  • τ²-bench (Telecom)
    19.0%
  • Humanity's Last Exam
    9.3%

An evaluation missing from this list was not run for this model — it is not a zero.

View as table
EvaluationScore
GPQA Diamond73.4%
AA-LCR (long context)58.7%
SciCode24.2%
τ²-bench (Telecom)19.0%
Humanity's Last Exam9.3%

Against its peers

Intelligence Index · this model highlighted, nearest peers in grey

Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.

View as table
ModelIntelligence
V3.225.1
Qwen3 Max24.5
Ling 3.0 Tiny (free)24.3
Claude Haiku 4.524.1
gpt-oss-120b24.1
Kimi K2 090524.0
o123.9
GLM 4.623.4

Percentile among all indexed models

Intelligence67th
Coding39th

Pricing

What it costs to run

List prices per million tokens, plus what one representative task works out to.

List price

Input / 1M tokens
Free
Output / 1M tokens
Free
Cached input / 1M
Not offered
Blended 3:1
Free

One task, estimated

Free

Input tokens
50,000
Output tokens
80,000
Profile
Reasoning

Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).

Serving providers

Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.