Model profile
InclusionAI
New in 2026

inclusionAI: Ling-2.6-flash

inclusionAI: Ling-2.6-flash is a budget-priced text-first model from InclusionAI with partial runtime data, large context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsN/A latencyLarge contextBudget pricing
Intelligence
14.2

Benchmark blend

Coding
25.3

Dev workflow signal

Context
262K Tokens

Large

Input Price
$0.10

Budget tier

Decision snapshot
45

inclusionAI: Ling-2.6-flash currently reads as a budget text-first option with large context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
14.2
14

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
262K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.10
86

$0.30 output / 1M

Efficient

Editorial Profile

inclusionAI: Ling-2.6-flash in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 25Math score N/A

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....

Identity

InclusionAI text-first profile

Positioning

Long-context research / Agent workflows with large context and partially published runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

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Context
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Input Price
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Context
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Input Price
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Intelligence
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Context
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
14.2
GPQA
59.3%
HLE
6.3%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
25.3
SciCode
27.1%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
57.4%
TAU2
86.0%
TerminalBench Hard
21.2%
LCR
28.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
262K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Other
Max Completion
32768
Moderation
No
Supported Parameters
frequency_penaltylogprobsmax_tokenspresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.10
Output
per 1M output tokens
$0.30
Blended
AA 3:1 mix
$0.15

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

OR Cache Read
$0.00

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.