Inkling 2 Small vs Kimi K3.1 Pro: Scores, Cost, Speed

Head-to-Head: Inkling 2 Small and Kimi K3.1 Pro on Intelligence Score 31 vs 46

Inkling 2 Small scores 31 on the Artificial Analysis Intelligence Index against 46 for Kimi K3.1 Pro, but it costs $1.18 per index task versus $3.68 and finishes each task in 18.5 minutes versus 36.3 minutes. Kimi K3.1 Pro is the stronger general model on current independent measurement, while Inkling 2 Small is the cheaper, faster, fine-tunable option — the right pick depends on whether you need peak capability or high-volume agent throughput at low cost. Inkling 2 Small launched on November 5, 2026, on Thinking Machines Lab's Tinker platform, with Apache 2.0 weights planned for the following week.

Key Takeaways

• Capability gap is 15 points. Artificial Analysis places Kimi K3.1 Pro at 46 and Inkling 2 Small at 31 on its Intelligence Index v5.
• Inkling 2 Small is roughly 3.1x cheaper per evaluated task ($1.18 vs $3.68) and about 2x faster per task (18.5 vs 36.3 minutes).
• Kernel coding favors Kimi K3.1 Pro. On Thinking Machines' own PMPP-Hard chart, Kimi K3.1 Pro averages 60.5 solved tasks out of 69; Inkling 2 Small averages 54.25.
• Inkling 2 Small has the clearer spec sheet: 594B total / 17B active parameters, 1M-token context, text and image input. Most Kimi K3.1 Pro specs are not confirmed in public reports reviewed here.
• Kimi K3 is not Kimi K3.1 Pro. Several launch posts compare Inkling 2 Small against Kimi K3; those numbers do not transfer.

Inkling 2 Small vs Kimi K3.1 Pro at a Glance

Dimension	Inkling 2 Small	Kimi K3.1 Pro
Developer	Thinking Machines Lab (official)	Moonshot (Kimi line)
Release date	November 5, 2026 (official)	October 23, 2026 (per a third-party benchmark tracker)
AA Intelligence Index	31 (Artificial Analysis)	46 (Artificial Analysis)
Cost per AA index task	$1.18	$3.68
Time per AA index task	18.5 min	36.3 min
PMPP-Hard kernel tasks (of 69)	54.25 (vendor chart)	60.5 (vendor chart)
Parameters	594B total / 17B active (official)	Unverified — no public number yet
Context window	Up to 1M tokens (official)	Unverified — no public number yet
Token pricing	$0.30 in / $0.03 cached / $1.00 out per 1M (Tinker)	Unverified — no public number yet
Input modalities	Text + image, no audio (official)	Unverified — no public number yet
Weights	Closed now; Apache 2.0 planned (official)	Unverified — no public number yet

Source labels matter here. Inkling 2 Small figures come from its official model card, launch post and Tinker pricing page. Kimi K3.1 Pro figures come almost entirely from Artificial Analysis comparison charts and one Thinking Machines benchmark chart.

Benchmarks: Where the 15-Point Gap Comes From

The cleanest shared measurement is the Artificial Analysis Inkling 2 Small page. Its comparison chart lists Kimi K3.1 Pro at 46 and Inkling 2 Small at 31. The index (v5.0.1) bundles nine evaluations, including Terminal-Bench 4.0, FrontierCode 2.0, HLE-Diamond, AA-Omniscience and GDP.pdf.

Inkling 2 Small's 31 is strong for its price class. Artificial Analysis ranks it #4 of 29 comparable models, against a class median of 23. The precise model-card figure is 30.6, rounded to 31. Artificial Analysis also stated in its launch analysis that the score is 18 points above the first-generation Inkling Small.

Kimi K3.1 Pro sits in a different tier on that same chart. It lands between Claude Fable 5.1 (49) and Muse Wonder (44), well above DeepSeek V4.1 Flash 1017 (33) and GLM-5.3 (32). On overall intelligence, Kimi K3.1 Pro is a frontier-adjacent model and Inkling 2 Small is a leading budget model.

The one coding test that names both models is Thinking Machines' PMPP-Hard Kernel Benchmark: 69 tasks, four independent attempts, a 10-hour budget. Kimi K3.1 Pro averaged 60.5 solved tasks; Inkling 2 Small averaged 54.25. GPT-6 Astra led at 63. Treat this as a vendor-run chart, though the vendor here published a result where its own model loses.

Source: @aitrackerbot

Design work is the only area where public posts put Inkling 2 Small ahead. DesignArena reported Inkling 2 Small at 4th overall and 1st among open-weights models with an Elo of 1376. A community post said this placed it ahead of Kimi K3.1 Pro, but no Kimi K3.1 Pro Elo figure appears in the sources reviewed. Read it as a community impression, not a measured margin.

Source: @0x0SojalSec

A note on Kimi K3 numbers

Inkling 2 Small's model card compares it against Kimi K3, not Kimi K3.1 Pro. Those rows include SWE-Bench Pro v2 (Hard) at 86.3% for Inkling 2 Small vs 88.2% for Kimi K3, and JobBench at 70.5% vs 54.3%. The widely shared "15x cheaper on output" claim also refers to Kimi K3. None of these figures describe Kimi K3.1 Pro. For a fuller picture of where Kimi K3.1 Pro sits against frontier closed models, see the earlier Kimi K3.1 Pro vs Claude Opus 5.5 breakdown.

Pricing and Cost per Task

Inkling 2 Small has published, confirmed pricing. Tinker serverless inference at 256K context costs $0.30 per 1M input tokens, $0.03 per 1M cached input tokens and $1.00 per 1M output tokens. Tinker fine-tuning costs $1.50 per 1M training tokens at 64K context and $3.00 at 256K.

Kimi K3.1 Pro per-token pricing does not appear in any source reviewed for this page. The comparable number available is Artificial Analysis's Cost per Index Task, which folds price and verbosity together.

Cost metric	Inkling 2 Small	Kimi K3.1 Pro
Input per 1M tokens	$0.30	Not yet confirmed
Output per 1M tokens	$1.00	Not yet confirmed
Cached input per 1M tokens	$0.03 (90% discount)	Not yet confirmed
AA cost per index task	$1.18	$3.68
Vals Index cost per test	$3.51	Not yet confirmed

On measured workloads, Inkling 2 Small does the same evaluated task for about 32% of Kimi K3.1 Pro's cost. That margin exists despite Inkling 2 Small being verbose. Artificial Analysis logged 154M output tokens across its index run, versus a class median of 89.8M.

Independent applied tests point the same way. A third-party coding-agent study scored Inkling 2 Small at 76% overall at $0.84 per long session, level with Claude Haiku 5.5 and Claude Sonnet 5.5. Surge AI's staff-level software benchmark called it the cheapest model tested at $0.34 per task. Neither test included Kimi K3.1 Pro.

Speed, Context and Architecture

Kimi K3.1 Pro is slower per task on Artificial Analysis: 36.3 minutes against 18.5 minutes for Inkling 2 Small. That is not raw throughput. Inkling 2 Small's output speed is 135 tokens per second, and its time to first answer token is 34.15 seconds, reflecting long reasoning before output.

Inkling 2 Small's architecture is fully documented in its model card:

• 48-layer decoder-only transformer with hybrid local and global attention
• Sparse MoE: 384 experts, 8 routed per token, 594B total / 17B active parameters
• 292M-parameter vision encoder based on Perception Encoder
• Variable Effort reasoning control (benchmarked at effort 0.99)
• MXFP8 and NVFP4 numerics; pretrained natively in NVFP4 on NVIDIA GB300 NVL72 racks
• Context window up to 1M tokens; RL training used 256K context with compaction

Training detail is unusually public. Pretraining covered 8.4T tokens in 68 hours. The main reinforcement-learning run lasted nine days on 2,048 GPUs and produced more than 17M rollouts. Coding made up 38.5% of rollouts. Thinking Machines' first-generation Inkling explainer covers how this lineage started.

Kimi K3.1 Pro's architecture, parameter count and context window are not documented in the sources reviewed. This page will update when Moonshot's figures are confirmed.

Availability and Access

Inkling 2 Small follows a Tinker-First Release. At launch it runs only on Tinker: inference-only access in Tinker Playground, plus fine-tuning and sampling through the Tinker API. Mira Murati confirmed same-day availability with weights to follow.

Thinking Machines calls this the Safe Path to Open Weights. Full Apache 2.0 weights are planned for the week after launch, contingent on external safety testing and red-teaming. Fine-tuned Tinker checkpoints become downloadable at that point. Early checkpoints were tested publicly on OpenCode under the codename Fledge-Alpha.

Self-hosting will be heavy. The 594B checkpoint needs storage and memory in DeepSeek-class territory, a point raised in the r/LocalLLaMA launch thread. No official VRAM guidance or quantization recipe has been published.

Kimi K3.1 Pro is already a shipping commercial model, released in late October 2026. Its official API terms and license are not covered in the sources reviewed. For teams that want to test it through a standard chat API, a Kimi K3.1 Pro model page is one route.

Which One Should You Use?

Choose Inkling 2 Small if:

• You run high-volume agent loops where $1.18 vs $3.68 per task compounds across thousands of runs.
• You need to fine-tune or RL-train a model and want Tinker's integrated training pipeline at $1.50–$3.00 per 1M training tokens.
• You plan to self-host under Apache 2.0 once weights ship, and have hardware for a 594B MoE.

Choose Kimi K3.1 Pro if:

• Raw capability matters most: 46 vs 31 on the AA Index is a wide margin.
• Your workload looks like hard systems coding, where Kimi K3.1 Pro leads PMPP-Hard 60.5 to 54.25.
• You need a model in production today with no pending weights-release gate.

Inkling 2 Small is the better buy per dollar, while Kimi K3.1 Pro is the better buy per task solved on hard problems.

Frequently Asked Questions

Is Inkling 2 Small better than Kimi K3.1 Pro?

No, not on overall capability. Artificial Analysis scores Kimi K3.1 Pro at 46 and Inkling 2 Small at 31. Inkling 2 Small wins on cost and speed, at $1.18 and 18.5 minutes per index task versus $3.68 and 36.3 minutes.

Is Inkling 2 Small cheaper than Kimi K3.1 Pro?

Yes, per evaluated task. Artificial Analysis measures Inkling 2 Small at $1.18 per index task versus $3.68 for Kimi K3.1 Pro. Inkling 2 Small lists at $0.30/$1.00 per 1M input/output tokens; Kimi K3.1 Pro token pricing is not confirmed in available sources.

Which is better for coding, Inkling 2 Small or Kimi K3.1 Pro?

Kimi K3.1 Pro leads the only shared coding test. On the PMPP-Hard kernel benchmark, it averaged 60.5 of 69 tasks against 54.25 for Inkling 2 Small. Broader head-to-head coding results are not yet published.

Is Inkling 2 Small open weights?

Not yet. It launched on November 5, 2026, with closed weights on Tinker, and Thinking Machines plans Apache 2.0 weights the following week after safety testing. Kimi K3.1 Pro's license status is not confirmed in available sources.

What is the context window of Inkling 2 Small vs Kimi K3.1 Pro?

Inkling 2 Small supports up to 1M tokens per its model card, with Tinker serverless pricing quoted at 256K. Kimi K3.1 Pro's context window is not confirmed in available sources.

Is Kimi K3 the same model as Kimi K3.1 Pro?

No. They are listed separately, with Kimi K3 scoring 55.75 on PMPP-Hard versus 60.5 for Kimi K3.1 Pro. Launch-day comparisons against Kimi K3 should not be read as Kimi K3.1 Pro results.

What to Watch Next

Three signals will move this comparison. First, the Apache 2.0 weights drop: if it lands on schedule, Inkling 2 Small becomes the top-scoring open model outside China on Artificial Analysis. Second, official Kimi K3.1 Pro specs and per-token pricing, which would fill the unverified cells above. Third, independent head-to-head runs on SWE-style and agentic benchmarks that include both models, not Kimi K3 as a stand-in.

Try related models on Kie.ai: DeepSeek-V4.1-Flash, Gemini 3.8 Flash and Claude Haiku 5.5.