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North Micro Vision Instruct

Released Aug 10, 2026 see all recent releases

Decision reading
North Micro Vision Instruct is tracked, but not publicly ranked yet. The profile exposes 1 sourced benchmark rows and leaves unsupported fields blank until a published record exists.

Data as of September 10, 2026 · How the score is built

Strongest published evidence

Published rows are visible, but no category has enough eligible evidence for a comparative rank.

Validate before choosing

1 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

Unranked

field median 56.2

Not eligible for a public rank

Price

Self-hosted; infrastructure cost varies

input median $1

No comparable first-party hosted token rate

Speed

Not measured

field median 86.5 tok/s

Time to first token not measured

Context

128Ktokens

field median 256,000

Maximum output length is tracked separately

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. AgenticNot measured
  2. CodingNot measured
  3. ReasoningNot measured
  4. KnowledgeNot measured
  5. MathNot measured
  6. MultilingualNot measured
  7. Multimodal1/1 verified
  8. Inst. FollowingNot measured
Verified sourceProvisionalNot measured

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
CohereLabs/North-Micro-Vision-InstructCohere North Micro Vision Instruct model card
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
Not sourced yet
Output modalities
Not sourced yet
Parameters
Not sourced yet
Availability
Cohere Labs publishes the 2.4B-parameter vision-language model (2B language backbone plus a 400M SigLIP 2-derived encoder) under Apache 2.0 on Hugging Face. The backbone supports a 128K context, but the validated multimodal operating range is 8K tokens.
Cloud regions
Not tracked yet
Lifecycle
Current
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing recordCohere North Micro Vision Instruct model card
Self-host
Open weights available; hardware estimate not sourced
Rate limits
Not tracked yet

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.

Estimate VRAM from known parameters

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticWeight 22%0 benchmarksNot measuredNot measured
CodingWeight 20%0 benchmarksNot measuredNot measured
ReasoningWeight 17%0 benchmarksNot measuredNot measured
KnowledgeWeight 12%0 benchmarksNot measuredNot measured
MathWeight 5%0 benchmarksNot measuredNot measured
MultilingualWeight 7%0 benchmarksNot measuredNot measured
MultimodalWeight 12%1 benchmarkVerifiedScore pending
Inst. FollowingWeight 5%0 benchmarksNot measuredNot measured

Benchmark ledger

Multimodal opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Multimodal1 row
Multimodal benchmark values, best verified comparison, weight, and source status
RealWorldQAScore62.2%Versus best verified row

Best verified: Qwen3.8-Flash-Next · 88.5%

Gap26.3 behindWeightDisplay only

Lineage

The sequence follows explicit supersedes links. A successor's displayed score stays at least 0.1 points above its predecessor; raw benchmark rows do not move. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Aug 10, 2026 · you are here

    North Micro Vision Instruct

    Not publicly ranked · Price not listed

Instruct

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

We track North Micro Vision Instruct, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.

North Micro Vision Instruct is a open weight model with a 128K context window. No explicit reasoning mode is documented in this profile.

Cohere Labs publishes the 2.4B-parameter vision-language model (2B language backbone plus a 400M SigLIP 2-derived encoder) under Apache 2.0 on Hugging Face. The backbone supports a 128K context, but the validated multimodal operating range is 8K tokens.

Official exact-value snapshot from the North Micro Vision Instruct Hugging Face model card (August 10, 2026). BenchLM maps RealWorldQA (0.622, stored as 62.2) onto its existing lane; MMBench, MMStar, GQA, and the multilingual MMBench rows have no exact compatible lane. The row stays unranked.

1 of 434 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Radar

North Micro Vision Instruct release history

Full release history

Radar confirmed these at the source. Use North Micro Vision Instruct in your work? Explore Radar to follow supported changes and choose your alerts.

Frequently asked questions

How does North Micro Vision Instruct perform overall in AI benchmarks?

North Micro Vision Instruct has 1 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.

Is North Micro Vision Instruct good for multimodal and grounded tasks?

North Micro Vision Instruct has source-displayable benchmark coverage for multimodal and grounded tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is North Micro Vision Instruct open source?

North Micro Vision Instruct is an open-weight model from Cohere. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

Does North Micro Vision Instruct have full benchmark coverage on BenchLM?

No. North Micro Vision Instruct currently has 1 source-displayable rows across 434 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of North Micro Vision Instruct?

North Micro Vision Instruct has a documented context window of 128K. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

Compare North Micro Vision Instruct with every tracked model482 comparisons

Last updated September 10, 2026. Runtime fields remain blank until a sourced snapshot exists.

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