Ling-2.6-flash
InclusionAI · released Apr 21, 2026
Ling-2.6-flash is an instant (instruct) model from inclusionAI with 104B total parameters and 7.4B active parameters, designed for real-world agents that require fast responses, strong execution, and high token efficiency....
Specification
- Context window
- 262K
- Max output
- 33K
- Knowledge cutoff
- Not stated
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 1
- Moderated
- No
- Uptime
- 100.0%
Intelligence
14.2
46th percentile
Coding
25.3
Coding Index
Agentic
2.3
Agentic Index
Output speed
86 t/s
Median across providers
Latency
614ms
Time to first token
Cost per task
$0.001
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
- τ²-bench (Telecom)86.0%
- GPQA Diamond59.3%
- IFBench57.4%
- AA-LCR (long context)28.0%
- SciCode27.1%
- Terminal-Bench Hard21.2%
- Humanity's Last Exam6.3%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| τ²-bench (Telecom) | 86.0% |
| GPQA Diamond | 59.3% |
| IFBench | 57.4% |
| AA-LCR (long context) | 28.0% |
| SciCode | 27.1% |
| Terminal-Bench Hard | 21.2% |
| Humanity's Last Exam | 6.3% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Mistral Medium 3.1Mistral AI14.7
- Solar Pro 3Upstage14.5
- Llama 4 MaverickMeta14.5
- 14.4
- Ling-2.6-flashInclusionAI14.2
- Gemini 2.5 FlashGoogle14.2
- 13.8
- 13.6
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Mistral Medium 3.1 | 14.7 |
| Solar Pro 3 | 14.5 |
| Llama 4 Maverick | 14.5 |
| Qwen3 VL 235B A22B Instruct | 14.4 |
| Ling-2.6-flash | 14.2 |
| Gemini 2.5 Flash | 14.2 |
| Qwen3 Next 80B A3B Instruct | 13.8 |
| Qwen3 Coder 30B A3B Instruct | 13.6 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.01
- Output / 1M tokens
- $0.03
- Cached input / 1M
- $0.002
- Blended 3:1
- $0.015
One task, estimated
$0.001
- Input tokens
- 50,000
- Output tokens
- 25,000
- Profile
- Standard
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.