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ModelCap

Flint

Official publisher releaseNewOpen weightsFree tier

Recursive·#69 of 355 on the current board·cheaper than #66 Kimi K2 Thinking

Flint is a language model from Recursive. It ranks #69 of 355 on ModelCap's current board, with an Index score of 61.6, modeled from the publisher's launch results against measured models. Its score range of 46.8–76.4 spans positions #27–#134. It is listed free of charge from one provider. It reads up to 262K tokens of context, and its weights are openly downloadable.

Figures as of 26 Oct 2026, 18:19 UTC · download the public dataset · methodology

ModelCap Index · Current board
61.6#69
Modeled from the publisher's launch results against measured models · range 46.8–76.4
Market Gravity
27.5Decreased by 26.3
Secondary market signal
Input
Free
per 1M tokens
Output
Free
per 1M tokens
Context
262K
tokens
Providers
1
endpoint

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
Recursivebf16FreeFree262Kbf16100.0%

ModelCap Index · Current board

Methodology
61.6
Modeled from the publisher's launch results against measured models
Current board
#69 of 355
Score range
46.8–76.4
Rank range
#27–#134
Technical details
Public rank basis
Modeled · peer benchmarks (architecture)
Evidence label
Modeled · peer benchmarks · 5 reported rows against measured corpus ladders (75%); global-corpus-prior over 211 held-out anchors (25%); shrunk 3 toward the measured corpus
Index support
23.4% · limited
Identity binding
Exact catalogue product · recursive/recursive/flint-v1.0 · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata RecursiveSI/flint@2a247be0c0aec895ccb0ac7ca92229ec6df37288 (not the evaluation revision)
Index observations used
0
Qualified Index evidence point
61.6
How this position was produced · fused launch placement

No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.

Fused estimate
61.6 ± 11.6
Independent share
0%
Shrink toward corpus
-3.0
Optimism haircut
None applied
Method
precision-weighted-v1
Channels fused into this model’s launch placement
ChannelEstimateShare
Corpus ladder · not independent
5 reported rows against measured corpus ladders
64.6 ± 13.375%
Publisher corpus prior · not independent
global-corpus-prior over 211 held-out anchors
52.3 ± 23.325%
Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability
—
Evidence status
No admitted measured evidence
General preference
—
Coding
—
Agents & tools
—
Reasoning
—
Evidence breadth
0%
Evidence coverage
0%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
27.2
Liquidity (providers × uptime)
22.5
Open reach (HF downloads)
13.4
Surface (context / tools / modalities)
75.3
Freshness
98.2
  • · OpenRouter popularity #70
  • · 1 live provider
  • · HF 30d downloads 8470
  • · Catalogue: context 262144, reasoning, tools
  • · First seen 2026-10-21

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Experimental capability estimate

Estimate policy
47.2
33.8–60.6 uncertainty interval
Excluded from rank
Confidence
80%
Training support
221 models · 129 lineages
Out-of-domain check
in domain
Feature coverage
100%
Lineage-held-out error
8.3 MAE

Experimental estimate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index
69.5
partial · 76% metadata coverage
Not capability
Artifact reproducibility
85.0
Access & legal clarity
100.0
Deployability
80.0
Evaluation provenance
0.0

Not yet published: the base model it derives from, admitted benchmark results, results across several capability areas, results from more than one source, a confident match to its benchmark entries, pinned evaluation dataset versions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage
Market Gravity
27.5
OpenRouter weekly popularity
#70 of 341

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

No matched Arena rating

This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.

Market Gravity breakdown

Usage55%
27.2

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
22.5

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
13.4

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
98.2

Time since first public availability, on a six-month half-life.

Specification

Model ID
recursive/flint-v1.0
Context window
262K tokens
Declared output cap
236K tokens

OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. OpenRouter metadata

Inputs
Text
Outputs
Text
Tokenizer
Other
Cached input tokens
Not offered
First seen on OpenRouter
21 Oct 2026

Capabilities

  • Supported: Reasoning
  • Supported: Tool use
  • Not supported: Structured output
  • Supported: Response format
  • Not supported: Moderated

Weights & access

Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.

Access
Open weights
Licence
apache-2.0
Pinned revision
2a247be0c0ae
Parameters
39.8B
Architecture
FlintForCausalLM
Model type
flint
Hugging Face downloads (30d)
8K
Downloads (all time)
8K
Likes
902
Repository updated
21 Oct 2026, 14:24 UTC

Alternatives to Flint, by the numbers

Ranked neighbours

Models within 3 places of #69 on the ModelCap Index.

Flint: common questions

How does Flint rank among AI models?

Flint holds ModelCap Index position #69 of 355 ranked language models as of 26 October 2026, with an Index score of 61.6 (interval 46.8–76.4); evidence: modeled · peer benchmarks. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does Flint cost per 1M tokens?

Flint is listed at Free per 1M input tokens and Free per 1M output tokens as of 26 October 2026. Prices come from the OpenRouter catalogue and re-observe on every refresh; at least one provider lists a free tier. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply Free total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of Flint?

Flint has a published context window of 262K tokens (262,144). The snapshot lists a 235,929 token catalogue-declared route output cap. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. The providers table lists each endpoint's context limit.

Is Flint open-weight?

Open weights (apache-2.0). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is RecursiveSI/flint on Hugging Face.

Which API providers serve Flint?

One provider serves Flint through OpenRouter as of 26 October 2026: Recursive. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Flint been evaluated on?

Flint's Index position rests on the evidence listed on this page; it has no result on the public boards ModelCap tracks as separate leaderboards as of 26 October 2026. The evidence panel names each source and its publication date.

When was Flint released?

Flint first appeared in the catalogue on 21 Oct 2026. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.