Inkling 2 Small

Today we are introducing our second model generation with Inkling 2 Small. It is a Mixture-of-Experts model with 594B total parameters, 17B active, and supports image inputs and variable effort.

Inkling 2 Small far surpasses our previous models’ scores on the Artificial Analysis Intelligence Index with a score of 31, reflecting gains in performance on agentic coding, vision, knowledge work, and reasoning. The increase in capabilities comes from improvements made at every step of our training process, including more efficient pre-training and a large, unified RL run.

Inkling 2 Small will initially be available through Tinker, with closed weights, following the approach we outlined in a safe path to open weights. We intend to make the full weights available under an Apache 2.0 license next week after the completion of external safety testing and red-teaming.

You can try Inkling 2 Small on Tinker Playground today. The model is also available for fine-tuning and sampling on Tinker; checkpoints will be downloadable with the release of Inkling 2 Small’s weights.

Inkling 2 Small is more capable across the board than our first generation, particularly in long-horizon autonomous work.

Inkling 2 Small’s vision and agentic capabilities enable it to incorporate visual feedback to iteratively build and refine websites, games, and 3D designs with interactive elements.

Inkling 2 Small was trained with RL on hundreds of thousands of long-horizon coding tasks across multiple agent harnesses and context-compaction strategies. We increased the quality of our synthetic coding data pipeline and moved beyond unit-test rewards alone; many tasks included an agentic RL grader that evaluated how the model approached the problem, not just whether its code passed. We also tested early checkpoints of Inkling 2 Small on OpenCode under the name Fledge-Alpha to assess our model’s performance on coding tasks in the real world.

The result is a model that is both stronger at coding and easier to use inside real agent loops. Inkling 2 Small improves on usability compared to Inkling: it follows instructions better, considers changes carefully, and verifies its work without being asked.

69 tasks · Four independent attempts · 10-hour budget

Hover a curve or select a model to inspect; select it again to reset. With a model focused, use ← and → to step through results, or Escape to clear.

PMPP-Hard resultsModelAUC (task-hours)Mean solved / 69Pass@4 / 69GPT-6 Astra616.396363Kimi K3.1 Pro583.8660.563Claude Opus 5.5499.656064DeepSeek V4.1 Flash497.3655.2562Inkling 2 Small494.2754.2564Mistral Large 4478.3950.2561Kimi K3453.5855.7566GLM 5.3430.9652.561GLM 5.2389.124261

Rows are ordered by AUC: the area under the mean curve over 0–10 hours, with a maximum of 690 task-hours.

Each model uses its maximum available reasoning setting, the PI harness and a shared compaction policy. We include fixes for task and grading issues identified during evaluation. Each submitted solution receives two independent LLM audits, with a third review resolving disagreements.

Beyond conventional software tasks, Inkling 2 Small was also trained on long-horizon autoresearch tasks. This included agentic GPU kernel optimization and ML tasks such as post-training a model autonomously, all run across a range of GPU architectures.

DeepSeek-V3.2 sparse attention, forward + backward

DeepSeek V4.1 Flash · 5.66×GLM 5.3 · 5.03×Mistral Large 4 · 3.43×

Speedup over the original (×)

DeepSeek Sparse Attention lets each query attend only to the top-k earlier tokens chosen by a lightweight indexer, so long-context cost no longer grows with the square of the context. Starting from the public TileLang kernels, make the training kernels faster while keeping their error close to the original's (tight tolerances), with no time limit. The benchmark covers several model sizes in different regimes, from packed pretraining sequences to long-context training. The public TileLang baseline is not tuned for B300, which leaves ample headroom on these workloads to measure each model's optimization capability and perseverance. Speedups are for these kernels alone, not end-to-end training.

To demonstrate its end-to-end autoresearch performance, we tested Inkling 2 Small on a task Proximal built for one of our internal benchmarks. Inkling 2 Small autonomously trained Qwen3.5-4B on Tinker to predict various time series such as weather and Wikipedia page views. It started by analyzing the tasks and writing its own evaluator, experimented with training datasets and recipes, and tried various approaches to SFT and RL, while debugging the process as it goes. After 4.5 hours of work and $426 in Tinker spend, the trained model has more than doubled its forecasting score.

"Less error" is compared with simply copying the last values forward.

Evaluation scores (% less error than copying)100 examples

$0.00Tinker spend, of $500

Data: U.S. Energy Information Administration (EIA-930); weather and air quality data by Open-Meteo.com (CC BY 4.0), containing modified Copernicus Climate Change Service and Copernicus Atmosphere Monitoring Service information 2026; Wikimedia pageviews (CC0).

Step 1: StartStep 2: Building evaluationStep 3: Data curationStep 4: Supervised Fine-TuningStep 5: First resultsStep 6: SFT on all historyStep 7: Reinforcement LearningStep 8: More SFTStep 9: Submission

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Start. Inkling 2 Small reads the task and the given raw data, covering years of hourly and daily history from five sources: electricity demand, wind and solar power, weather, air quality and Wikipedia page views.Building evaluation. Before any training, Inkling 2 Small builds an evaluation script to measure progress. The baseline is copying the latest values forward.Data curation. It turns the history through May into 12,500 training problems.Supervised fine-tuning. It starts its first supervised fine-tuning run (SFT 1). Meanwhile it finds a bug in its evaluation: it gave all 8 forecasts the same random seed, so they came out identical, while the final grader uses 8 different seeds.First results. After SFT 1, the model makes less error than copying on every data source: 25% overall, up from 19%. It also notices that evaluations are expensive ($101 so far) and cuts their cost by asking for all 8 forecasts in one request, so the prompt is prefilled once.SFT on all history. It retrains on all the history up to July 31, and saves this model as its submission so far.Reinforcement learning. It tries reinforcement learning by using the task's score as reward. The forecasts become overconfident and the score drops (26%, then 23%), so it stops the run. A quick check shows the spread of its SFT 2 forecasts (i.e., how uncertain they are) is already about right, so reinforcement learning had little to improve.More SFT. A second epoch over the same data does not help (SFT 3: 25%). A short extra run on only the last 12 months, the period closest to the test, gives its best model (SFT 4: 29%). The last two years (SFT 5) or seven months (SFT 6) resulted in worse performance.Submission. It submits its best SFT checkpoint (SFT 4) and stops after 4 h 17 min, having spent $426 of its $500 budget. On the unseen August–September data, this final model makes about 15% less error than the base model.

Vision interpretation and creation

Inkling 2 Small processes image inputs using a 292M-parameter vision encoder based on Perception EncoderBolya et al., 2025.. We use 20×20-pixel patches with 2×2 spatial pooling, and interleave image patches with text tokens. The model demonstrates strong visual capabilities across multiple categories, including OCR, grounding, infographic interpretation, and visual reasoning.

The model uses vision in the loop to interpret and validate the visual coding output it generates. Pending the release of its weights, it is set to be the top open-weights model on DesignArena.

We even tested Inkling 2 Small against some of our researchers in a wine tasting event. It outperformed them using vision alone, correctly identifying three wines from a single picture in 19 out of 20 trials.

Prompt: you’re in a blind wine tasting competition. glasses are labeled 1 to 3 from left to right. varietals: 99 cab sauv, 19 cab sauv, 23 nebbiolo

Inkling 2 Small: Here’s my read — with the big caveat up front that color from a phone photo under warm restaurant lighting is shaky evidence, so treat this as an educated guess, not a verdict.

My guess:Glass 1 = 2019 Cabernet SauvignonGlass 2 = 1999 Cabernet SauvignonGlass 3 = 2023 Nebbiolo

The honest weak points in that guess: lighting is uneven (the left glass sits in the dimmer zone near the hearth, which exaggerates its darkness), and a well-stored 1999 can drink much darker than it looks on camera. Hue differences, not depth, would be the real tell — if glass 2 shows orange/brown at the rim versus glass 1’s purple-red edge, that confirms it.

Fast sensory tie-breakers when you actually taste:

If the pour in glass 3 is pale but your mouth feels sanded down by tannins, that’s the slam-dunk nebbiolo confirmation; if it is soft and plush, flip glass 3 and glass 2. Good luck in the competition — report back with the reveal if you get it.

Inkling 2 Small accelerates knowledge work in realistic professional environments. It proficiently looks up information, calls tools over MCP, and produces high-quality deliverables. On AA-Briefcase, an end-to-end knowledge work benchmark, these capabilities come together for an ELO score of 1527.

Inkling 2 Small is especially strong in finance and quantitative reasoning over spreadsheets, filings and documents. Among open weight models, Inkling 2 Small ranks first on AA-AnalystAgent and Dayjob:Finance, and second on Vals Finance Index and DiligenceBench.

Some models respond better to fine-tuning, independent of their out-of-the-box performance. We wanted Inkling 2 Small to be a strong base model for customization.

Inkling 2 Small learns faster and improves more than our previous models when trained on the same data and algorithm across a variety of post-training scenarios, which we curated based on customer use cases. For example, starting from similarly low scores on a public financial-document summarization task, Inkling 2 Small adapts quickly, reaching the final performance of Inkling Small with just 75 training steps and finishing substantially ahead.

00.20.40.60.81$0$500$2,000$8,000Held-out rubric rewardEstimated Training Cost

Inkling 2 Small learns faster and finishes at higher performance than our previous models, and is competitive with other OSS models of similar size in an RL fine-tuning setting. We used public earnings call data to create a task of investment analysis with expert judgement, similar to the type of financial analysis workloads our customers use tinker for training custom models. To ensure a fair comparison, we ran a learning rate sweep for each model; all models were trained on Tinker with Tinker Cookbook abstractions using our Async RL implementation with the Stable Reinforce loss, over 200 steps. Results for the Inkling models are averaged over 3 seeds; the other models use single runs. Costs are estimated by multiplying the number of sampling and training tokens with current tinker per-token pricing; for sampling, we assume prefix cache hits within rollouts among the same group.

Reasoning, knowledge, and epistemics

Inkling 2 Small is trained on challenging STEM problems, instruction following, information seeking with and without web search, and forecasting tasks. During RL, we use proper scoring rules for forecasting tasks and uncertainty-sensitive rewards on some question-answering tasks. This trained the model to assess uncertainty, express appropriate confidence, and make well-calibrated predictions.

Our safety work for Inkling 2 Small centers on two questions: how the model performs for the general user, and how well it resists the removal of safeguards by a malicio