What Is Inkling 2 Small? Specs, Costs & Benchmarks

Inkling 2 Small 101: The 594B Mixture-of-Experts Architecture Activating 17B Parameters

Inkling 2 Small is a multimodal Mixture-of-Experts (MoE) transformer developed by Thinking Machines Lab that activates 17 billion parameters out of 594 billion total parameters for each processed token. Announced on November 5, 2026, the model features native image processing, variable inference effort, and an input context window of up to 1 million tokens. It is available immediately for hosted inference and fine-tuning on Thinking Machines' Tinker platform, with standalone model weights scheduled for release under an Apache 2.0 license following external red-teaming.

Key Takeaways

• Sparse MoE Design: Features 594 billion total parameters distributed across 384 experts, routing 8 experts per token to maintain an active footprint of 17 billion parameters.
• Hardware-Native Precision: Trained natively in NVFP4 precision across NVIDIA GB300 NVL72 racks, supporting both NVFP4 and MXFP8 execution.
• Tinker Pricing: Costs $0.30 per million input tokens, $0.03 per million cached input tokens, and $1.00 per million output tokens for serverless inference.
• Performance Jump: Scored 31 on the Artificial Analysis Intelligence Index v5, an 18-point gain over its predecessor, Inkling-Small.
• Conditional Open-Weights Stance: Operates as a hosted proprietary model on Tinker today, with full weights promised under an Apache 2.0 license once external safety reviews conclude.

What Is Inkling 2 Small?

Inkling 2 Small represents the first release in the second generation of foundational models from Thinking Machines Lab, the AI research organization founded by former OpenAI Chief Technology Officer Mira Murati. While the "Small" moniker suggests an edge-oriented model, the name reflects active runtime compute rather than static storage size.

The architecture targets long-horizon agentic systems, software development workflows, multimodal verification, and autonomous tool usage. Early checkpoints were tested publicly in coding environments under the test designation Fledge-Alpha. Compared to the dense and semi-sparse architectures of the previous generation, examined in our earlier analysis of the original Inkling, Inkling 2 Small relies on high-granularity expert routing to scale knowledge capacity without demanding linear compute scaling per token.

At present, Inkling 2 Small is in a hosted availability window. Users can query and fine-tune the system via Tinker APIs and web interfaces, while the model weights remain gated behind external red-teaming procedures.

Inkling 2 Small at a Glance

Specification	Technical Detail
Developer	Thinking Machines Lab, Inc.
Release Date	November 5, 2026
Model Architecture	48-layer decoder-only sparse Mixture-of-Experts (MoE)
Total Parameters	594 billion
Active Parameters	17 billion per token
Expert Routing	8 routed experts chosen from 384 total experts
Context Window	Up to 1,000,000 tokens (64K and 256K tiers on Tinker)
Input Modalities	Text (UTF-8) and Image (pixel-based, 40px to 4096px)
Output Modality	Text (UTF-8)
Native Precision	NVFP4 pretraining; MXFP8 and NVFP4 inference support
Serverless Pricing	$0.30 / 1M input, $0.03 / 1M cached input, $1.00 / 1M output
Fine-Tuning Cost	$1.50 / 1M tokens (64K context), $3.00 / 1M tokens (256K context)
Current Availability	Hosted API and Playground via Tinker
Target License	Apache 2.0 (weights pending safety testing)

Source: @TeksEdge

How Inkling 2 Small Works

Inkling 2 Small splits computation between a shared autoregressive transformer base and specialized parameter clusters. It avoids calculating the full parameter graph on every forward pass, decoupling parameter volume from generation latency.

Expert Routing and Attention Design

The model utilizes a 48-layer decoder-only transformer backbone. For every token processed, an internal router evaluates token state and dispatches computation to 8 of 384 available experts. This fine-grained distribution scheme yields an active compute cost roughly equivalent to a 17-billion-parameter dense model, though local execution will demand hosting memory sized for the entire 594-billion parameter state.

The attention mechanism implements a hybrid design that alternates between local attention windows and global attention layers. This structure preserves context recall up to 1 million tokens while curbing the quadratic memory overhead typical of pure global self-attention across ultra-long prompts.

Native Vision Integration

Visual capability is handled by an integrated 292-million-parameter vision encoder based on Perception Encoder. Incoming images (supported between 40px and 4096px) are partitioned into 20-by-20-pixel patches, followed by a 2-by-2 spatial pooling stage. These visual tokens project directly into the transformer's shared hidden dimension, processing natively alongside text tokens rather than through a secondary vision-language adapter. Notably, audio support found in the earlier Inkling line was omitted from Inkling 2 Small.

Pretraining and Unified Reinforcement Learning

Thinking Machines structured the training pipeline across four progressive stages followed by a single reinforcement learning (RL) campaign:

1. Initial Pretraining: 8.4 trillion tokens composed of natural language, source code, and image datasets, trained in 68 hours on NVIDIA GB300 NVL72 racks using native NVFP4 precision.
2. Extended Pretraining: 3.8 trillion tokens of curated material processed at a 512,000-token context length.
3. Reasoning and Trajectories: 320 billion tokens of multistep agent traces executed at 1 million tokens of context.
4. Targeted Refinement: 34 billion tokens targeting high-density agent trajectories.

Following pretraining, the team executed a unified 9-day reinforcement learning run on 2,048 GPUs. The run covered 1,075 optimization steps and generated over 17 million rollouts using more than 3 trillion tokens. Coding accounted for 38.5% of total rollouts and 65% of RL compute. The lab paired this with a length penalty and variable reasoning effort controls (benchmarked at 0.99 effort) to balance generation length against solution accuracy.

Source: @aitrackerbot

Alignment data disclosed by the lab indicated that internal misbehavior metrics fell from 66.8 points on the previous Inkling generation to 14.9 points on Inkling 2 Small, accomplished through alignment rewards and self-distillation from a conduct-specification teacher model.

What You Can Do With Inkling 2 Small

The high context threshold and variable reasoning controls make Inkling 2 Small suited for sustained, multi-turn technical assignments rather than standard conversational chat.

Autonomous Model Post-Training

In an experimental demonstration published by Thinking Machines, Inkling 2 Small executed an autonomous model-training workflow. Tasked with improving Qwen3.5-4B on time-series forecasting within a $500 Tinker resource budget, Inkling 2 Small generated 12,500 synthetic training pairs, built a custom validation harness, and debugged an evaluation-seeding failure. When reinforcement learning passes degraded downstream performance, the model halted the branch and selected an optimal supervised fine-tuning checkpoint. The session completed in 4 hours and 17 minutes, consuming $426 in compute and lowering validation error by approximately 15% on unseen data.

Procedural 3D and Web Construction

Inkling 2 Small leverages its vision encoder and reasoning loop to verify visual outputs iteratively. In one official build test, the model developed a Three.js procedural city simulation without pre-rendered assets. It generated geometric structures, textures, and vehicle trajectories procedurally, updating source logic based on visual checks until power grid and budget systems functioned inside the browser canvas.

Diligence and Domain Research

Evaluators tested the model on complex information-retrieval pipelines. On financial analysis tasks, an automated research workflow run by third-party evaluators produced a comprehensive public-equity diligence document for $0.69 in token expenditure. On domain evaluations, the model registered 73.89% on the Vals Legal Index and 61.47% on the Vals Finance Index.

How Inkling 2 Small Compares

Third-party tracking platforms such as Artificial Analysis evaluated Inkling 2 Small across coding, reasoning, and system latency. While Inkling 2 Small leads Western open-weight benchmarks, it encounters strong competition from top-tier Chinese models.

Benchmark / Metric	Inkling 2 Small	DeepSeek-V4.1-Flash	GLM-5.3-Flash	Kimi K3	Claude Haiku 5.5
Artificial Analysis Index	31 (30.6)	32.6	27.8	–	36.5
FrontierCode v2.0	25.4%	28.5%	14.2%	17.3%	34.9%
DeepSWE v1.1	72.1%	77.0%	63.4%	68.5%	59.9%
SWEBench Pro v2 (Hard)	86.3%	–	–	88.2%	–
Vals Index v2.2	56.1%	61.3%	50.9%	53.0%	58.7%
DesignArena Elo	1,340	–	–	–	–
Input Price / 1M tokens	$0.30	$0.60	$0.84	–	$1.00
Output Price / 1M tokens	$1.00	$3.60	$2.11	–	$5.00

On the Artificial Analysis composite index, Inkling 2 Small scored 31, surpassing the original Inkling-Small score of 13 by 18 points. It trails DeepSeek-V4.1-Flash (32.6) on raw reasoning and coding density, but undercuts it on hosted output pricing ($1.00 versus $3.60 per million tokens).

An independent evaluation conducted by developer tool provider Bito tested Inkling 2 Small against the Claude family across five-turn software development scenarios. Inkling 2 Small posted an overall score of 76%, landing between Claude Haiku 5.5 (77%) and Claude Sonnet 5.5 (75%). The analysis reported that Inkling 2 Small excelled at tracing legacy source code (92% accuracy versus Sonnet's 87%), but lagged behind during implementation and patching phases, where its plan quality fell short of Sonnet by 15 points.

Availability: How to Access Inkling 2 Small

Developers can interact with Inkling 2 Small through official channels:

1. Tinker Playground: A browser-based interface for low-latency sampling, zero-setup prompt experimentation, and reasoning-effort adjustment.
2. Tinker Inference API: REST-compatible endpoints providing access for programmatic calls. Standard serverless rates are set at $0.30 per 1M input tokens, $0.03 per 1M cached input tokens (a 90% discount), and $1.00 per 1M output tokens at 256K context.
3. Tinker Fine-Tuning: Organizations can execute custom LoRA or full-rank fine-tuning jobs on Tinker using the tml-renderers package. Tuning costs are billed at $1.50 per million tokens for 64K context windows and $3.00 per million tokens for 256K context windows. Checkpoints created during this stage will become downloadable when weight distributions unlock.

The release of local checkpoints remains subject to a temporary safety gate. Independent community discussions on r/LocalLLaMA emphasize that deployment of the complete 594B model will require multi-GPU nodes with substantial VRAM, even after community quantizations emerge.

What We Don't Know Yet

Because Inkling 2 Small debuted under a staged rollout, several operational details remain unverified:

• Local Deployment Requirements: Thinking Machines has not yet published memory guidelines, minimum hardware topologies, or official quantization profiles for self-hosting a 594B MoE model.
• Definitive Open-Weights Release Date: The team stated an intention to publish Apache 2.0 weights "next week," but the completion date of external red-teaming checks has not been formalized.
• Inference Latency Profile: Independent runtime data on time-to-first-token (TTFT) and throughput under sustained load is still sparse. In Artificial Analysis benchmarks, task evaluations took an average of 18.5 minutes with heavy reasoning verbosity (154 million evaluation tokens generated), indicating that high reasoning effort requires significant patience.

Frequently Asked Questions

Is Inkling 2 Small open source?

Inkling 2 Small is currently accessible only via API and web playground on Thinking Machines Lab's Tinker platform. The lab announced that full model weights are planned for open release under an Apache 2.0 license following safety checks and red-teaming.

How much does Inkling 2 Small cost to run?

Inkling 2 Small costs $0.30 per million input tokens, $0.03 per million cached input tokens, and $1.00 per million output tokens on Tinker's serverless inference tier at a 256K context length. Fine-tuning runs cost $1.50 per million tokens for 64K context and $3.00 per million tokens for 256K context.

How many parameters does Inkling 2 Small have?

Inkling 2 Small contains 594 billion total parameters in a sparse Mixture-of-Experts architecture, activating 17 billion parameters per token across 384 experts. This design allows it to run per-token calculations at the cost of a smaller model while preserving the broad knowledge base of a larger system.

Does Inkling 2 Small support vision and audio?

Inkling 2 Small natively supports text and image inputs using a 292-million-parameter vision encoder. Unlike the first-generation Inkling model, Inkling 2 Small does not support audio inputs.

Who created Inkling 2 Small?

Inkling 2 Small was developed by Thinking Machines Lab, an artificial intelligence research laboratory founded by former OpenAI Chief Technology Officer Mira Murati.

How does Inkling 2 Small compare to DeepSeek-V4.1-Flash on coding?

Inkling 2 Small scores 25.4% on FrontierCode 2.0 and 72.1% on DeepSWE v1.1, while DeepSeek-V4.1-Flash leads with 28.5% on FrontierCode 2.0 and 77.0% on DeepSWE v1.1. However, Inkling 2 Small offers cheaper API output pricing at $1.00 per million tokens compared to $3.60 for DeepSeek-V4.1-Flash.

What to watch next: Track the public release of the standalone Apache 2.0 weights on model hubs, monitor initial vLLM execution performance for the 384-expert MoE design, and watch whether downstream coding harnesses can optimize its plan-generation phase to match its high code-reading accuracy.

Try related models on Kie.ai: DeepSeek-V4.1-Flash, Claude Haiku 5.5 and Kimi K3.1 Pro.