Inkling-Small

Introducing Inkling-Small

Jul 30, 2026

Try on Tinker

Model card

Hugging Face

Today, we are releasing Inkling-Small, an efficient open-weights model that achieves comparable performance to Inkling at a quarter of its size.

Inkling-Small is a Mixture-of-Experts transformer with 276B total parameters, 12B active, trained on NVIDIA GB300 NVL72 systems. Like Inkling, it features native reasoning over audio and images, variable thinking effort, a context window of up to 1M tokens, and well-rounded performance across a range of benchmarks.

Compared to Inkling, Inkling-Small achieves comparable performance with much less compute. Across agentic tool use (Terminal-Bench 2.1), reasoning (HLE text-only, no tools), and instruction following (IFBench) benchmarks, Inkling-Small is more efficient than Inkling and competitive with other models in its weight class. Furthermore, its variable thinking effort lets users easily adapt it to target their use case, balancing cost and performance.

Inkling-Small 12B active (effort sweep) Inkling 41B active (effort sweep) Comparison models

Inkling-Small 12B active (effort sweep) Inkling 41B active (effort sweep) Comparison models

Performance-Compute Comparison. Sweeping reasoning effort from minimal to xhigh traces the performance-compute curve (output TFLOPs per sample) for Inkling-Small and Inkling on Terminal-Bench 2.1, HLE (no tool), and IFBench. We show that Inkling-Small is competitive with other open-weights models in a similar size range on both performance and efficiency. Output TFLOPs per sample are estimated as 2 × active parameters × mean generated tokens per sample, where generated-token counts include reasoning tokens and come from our evaluations or public reports from Artificial Analysis.Performance-Cost Comparison. Sweeping reasoning effort from minimal to xhigh traces the performance-cost curve (dollar output price per sample) for Inkling-Small and Inkling on Terminal-Bench 2.1, HLE (no tool), and IFBench. We show that Inkling-Small is competitive with other open-weights models in a similar size range on both performance and efficiency. Estimated output cost per sample is computed as mean generated tokens per sample × output price per token, where generated-token counts include reasoning tokens and come from our evaluations or public reports from Artificial Analysis (reasoning + answer tokens only). Inkling output pricing is $4.05 / 1M tokens and Inkling-Small output pricing is $1.20 / 1M tokens; for comparison-model pricing, we use the model provider’s official pricing when possible. When this is not possible, we use pricing from the recommended third party inference provider or pricing from Artificial Analysis, whichever is lower.

We are releasing the full weights of Inkling-Small. We’re also making it available for fine-tuning on Tinker, and for text, image, and audio chat on Tinker Playground.

Capabilities

As we build our model family, we are always iterating on our approach. Inkling-Small began training after its larger counterpart, which let us improve its training process. For example, we made changes to Inkling-Small’s pre-training data mix and machine learning recipe. Additionally, we post-trained an earlier checkpoint, Inkling-Small (preview), in part using on-policy distillation with Inkling as the teacher. Starting from that checkpoint, we continued scaling agentic coding RL for two weeks. With these improvements, Inkling-Small surpassed Inkling on reasoning and agentic coding benchmarks. Inkling maintains an advantage on knowledge coverage and factuality.

Inkling-Small is a broad, balanced generalist model. Benchmark scores are shown on a shared 0–100 scale; higher is better.

Reasoning and Agentic Tasks

Inkling-Small matches or exceeds Inkling on reasoning and agentic tasks. On Humanity’s Last Exam it scores 31.6%, ahead of Inkling’s 29.7%, and the advantage holds at every thinking budget: Inkling-Small’s test-time compute curves sit above Inkling’s throughout. On SWEBench-Verified it exceeds 80%.

Across many reasoning and agentic benchmarks, Inkling-Small at max reasoning effort has a strong performance-token tradeoff when compared to open weights models in its weight class.

Inkling-Small Inkling Comparison models Pareto frontier

Output tokens/task

Output tokens/task

Output tokens/task

Output tokens/task

Output tokens/task

Token-Efficiency Performance Tradeoff on Reasoning + Agentic Benchmarks. We evaluate Inkling-Small and Inkling (with max effort) along with other open-weights models on agentic and reasoning tasks (GDPval-AA v2, τ³-Banking, AA-Briefcase and CritPt) and show performance and output token length (including reasoning + answer). Inkling-Small is among the most efficient open weights models, marked by the dashed line. Results were obtained from our evaluation or reference data from Artificial Analysis.

Inkling-Small also runs smoothly across a variety of coding and agent harnesses, making it a cost-efficient choice for coding and tool-use workflows.

Multimodality

Like Inkling, we crafted Inkling-Small for audio intelligence, making it a good candidate for real-world audio applications. We also improved its ability to use Python for visual tasks. The model can combine visual reasoning with operations such as cropping, zooming, and programmatic image inspection, improving usability on documents and charts where relevant information may be small or difficult to inspect directly.

Inkling-Small uses the same natively multimodal encoder-free architecture as Inkling. Audio is represented as dMel spectrograms, while images are divided into 40×40-pixel patches and transformed using a four-layer hMLP. Both are transformed via a light-weight embedding layer and processed jointly with text tokens. Inkling-Small nearly matches Inkling across most multimodal evaluations at a lower cost. It retains strong performance on visual reasoning, chart and diagram understanding, mathematical visual question answering, speech understanding, and longer-form audio reasoning.

Open weights

Closed weights

Inkling-Small

MiMo V2.5

Nemotron-3Nano-Omni

Qwen3-Omni

Qwen3.5397B-A17B

Qwen3.5Omni-Plus

Gemini 3.5Flash-Lite

Vision

MMMU Pro (Standard 10)

74.0% 75.4% 53.0% 60.0% 77.3% 71.0% 79.0%

Charxiv RQoriginal / with python

77.4/81.3% 81.0%/– 63.6%/– 61.1%/– 80.8%/– 72.5%/– 70.0%/–

Audio

Audio MC

54.9% 30.4% 23.2% 24.3% – 37.6% 33.6%

MMAU

77.0% 73.6% 76.7% 77.5% – 81.1% 75.2%

VoiceBench

90.1% 86.4% 89.4% 88.8% – 92.4% 85.9%

Audio and vision benchmarks against specialist omni models (open- and closed-weight), reported at effort=0.99.

Epistemics

Inkling-Small was trained similarly to Inkling on epistemics, focusing on calibration, instruction following, and resistance to censorship. Inkling-Small matches Inkling’s performance on forecasting. Calibration involved RL against proper scoring rules on a large corpus of real-world forecasting questions, improving its ability to express appropriate confidence and produce calibrated forecasts under uncertainty.

Open weights

Closed weights

Inkling-Small

Inkling

Kimi K2.6

GPT-5.5

Claude Opus 4.8

Gemini 3.1 Pro

Grok 4.3

Forecasting

ForecastBenchno search · Brier Index ↑

61.3 ± 0.46 60.1 ± 0.54 58.8 ± 0.41 59.3 ± 0.33 56.2 ± 0.71 60.6 ± 0.43 60.9 ± 0.38

ForecastBenchwith search · Brier Index ↑

61.5 ± 0.54 61.0 ± 0.56 – 64.3 ± 0.79 59.9 ± 0.38 61.9 ± 0.69 61.3 ± 0.54

Prophet ArenaBrier Score ↓

0.1238 ± 0.0086 0.1276 ± 0.0092 0.1265 ± 0.0093 0.1179 ± 0.0089 0.1181 ± 0.0087 0.1155 ± 0.0084 0.1264 ± 0.0089

ForecastBench and Prophet Arena results were obtained during testing between July 19 and July 28, 2026.

Safety

Inkling-Small inherited the same safety post-training recipe as Inkling, with built-in safeguards covering our internal spec of safety. These include everyday human-AI interactions as well as dual-use capabilities. Inkling-Small also underwent the same pre-deployment testing process, comprising both internal evaluations and red-teaming by trusted external partners.

On StrongREJECT, which measures whether models refuse unambiguously harmful requests, Inkling-Small is on par with Inkling, and matches the performance of existing open-weights models. On FORTRESS, which measures safety in settings spanning crime, violence, and dual-use risks, it is competitive in both refusal and over-refusal.

Inkling-Small

Inkling

Nemotron 3Ultra

Qwen3.5397B-A17B

MiMo V2.5

Minimax M2.7

DeepSeek V4Flash

FORTRESSadversarial

71.6% 78.0% 77.6% 77.3% 64.8% 86.3% 32.0%

FORTRESSbenign

96.9% 95.9% 90.6% 95.4% 94.6% 90.1% 99.2%

StrongREJECT

98.4% 98.6% 98.7% 99.4% 99.3% 99.4% 97.4%

Safety benchmarks, reported at effort=0.99; higher is better throughout. FORTRESS adversarial is the rate of refusing harmful requests, benign the rate of still answering safe ones.

Benchmarking Inkling-Small

Like Inkling, we benchmarked Inkling-Small on a broad range of capabilities. All evals run at effort 0.99 and temperature 1.0. All coding evals run with 256K max-token trajectory limit, similar to Inkling.

To improve consistency, we rely on externally reported evaluations for both internal and external models when applicable. Specifically, we use the scores reported by:

Artificial Analysis: Humanity’s Last Exam, GPQA Diamond, SciCode, GDPval-AA v2, Tau 3 Banking, AA Omniscience, MMMU Pro, AA-Briefcase

Scale AI: AudioMC, MCP Atlas

ARC Prize: ARC-AGI v1 and ARC-AGI v2

Forecasting Research Institute: ForecastBench

ProphetArena

Open weights

Closed weights

Inkling-Small

Qwen3.5397B-A17B

MiMo V2.5

Minimax M2.7

DeepSeek V4Flash

Nemotron 3Ultra

Inkling

Claude 4.5Haiku

Gemini 3.5Flash-Lite

GPT 5.6Luna

Model Info

AA Indexv4.1

40.0% 34.0% 37.0% 38.0% 40.0% 38.0% 41.0% 30.0% 36.0% 49.0%

Params (B)activated / total

12 / 276 17 / 397 15 / 310 10 / 230 13 / 284 55 / 550 41 / 975 – – –

Agentic (coding)

SWEBench Verified*

80.2% 76.4% 71.0% 79.9% 79.0% 70.7% 77.6% 73.3% 75.0% 93.0%

SWEBench Propublic

55.9% 50.9% 56.1% 56.2% 52.6% 46.4% 54.3% 39.5% 54.2% 62.7%

Terminal Bench 2.1*best harness

64.7% 51.3% 63.7% 55.4% 61.8% 56.4% 63.8% 44.2% 54.0% 82.5%

SciCode

48.7% 42.0% 43.1% 47.0% 44.9% 39.9% 46.1% 43.3% 40.9% 50.0%

Agentic (general)

GDPval-AA v2

1269 962 1145 1159 1189 1164 1238 911 1139 1530

MCP Atlaspublic / all

79.6/79.2% 74.2%/– – 49.4%/– 69.0%/– 47.4/44.7% 78.8/76.0% 41.2/40.2% 79.8/76.8% 77.0/75.0%

Tau 3 Banking

15.5% 13.4% 6.6% 8.9% 22.9% 13.8% 23.7% 9.1% 16.5% 24.3%

BrowseCompwith context management

77.4% 78.6% – 76.3% 73.2% 63.0% 77.1% – – 84.0%

Toolathlon Verified

54.4% 40.7% 49.1% 47.5% 50.9% 34.3% 45.5% 26.9% 57.1% 67.9%

AA-Briefcase

917 – – – 833 870 839 612 – –

Reasoning (general)

GPQA Diamond

89.5% 89.3% 84.9% 87.4% 89.4% 86.7% 87.2% 67.2% 83.8% 89.5%

HLEtext only

31.6% 27.3% 25.2% 28.1% 32.1% 26.6% 29.7% 9.7% 17.5% 35.6%

HLE†with tools

47.8% 48.3% 40.0% 40.3% 45.1% 37.4% 46.0% 17.8% 42.5% 48.9%

AIME 2026

95.5% 93.3% 93.6% 87.7% 95.8% 94.2% 97.1% 85.1% 82.2% 97.6%

HMMT Feb 2026

90.2% 87.9% 82.6% 71.2% 93.9% 78.8% 86.3% 66.7% 63.6% 98.5%

CritPt

8.3% 1.7% 3.7% 0.6% 7.1% 3.1% 5.4% 0.0% 0.0% 20.6%

Reasoning (abstract)

ARC-AGI-1

84.0% – – – – – 79.5% 47.7% – 87.7%

ARC-AGI-2

40.1% – – – – – 36.5% 4.0% – 47.6%

Factuality

SimpleQA Verified

20.6% 26.0% 16.1% 13.5% 34.1% 32.4% 43.9% 5.9% 44.1% 41.7%

AA Omniscienceindex

-9.0 -29.8 -9.3 0.7 -22.9 -1.0 2.1 -4.2 6.9 -11.6

Chat

IFBench

82.2% 78.8% 67.1% 75.7% 79.2% 81.4% 79.8% 54.3% 78.6% 67.3%

Global-MMLU-Lite

86.7% 90.0% 83.5% 83.9% 88.4% 85.6% 88.7% 83.4% 89.4% 88.7%

Safety

StrongREJECT

98.4% 99.4% 99.3% 99.4% 97.4% 98.7% 98.6% 98.6% 97.6% 98.7%

FORTRESSadversarial

71.6% 77.3% 64.8% 86.3% 32.0% 77.6% 78.0% 91.3% 70.7% 83.8%

FORTRESSbenign

96.9% 95.4% 94.6% 90.1% 99.2% 90.6% 95.9% 94.1% 95.5% 97.8%

Vision

MMMU ProStandard 10

74.0% 77.3% 75.4% – – – 73.5% 58.6% 79.0% 78.6%

Charx

[truncated for AI cost control]