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Data

Data as of November 12, 2026 · How the score is built

Granite-4.0-350M

EstablishedOpen WeightNon-Reasoning

Decision readingGranite-4.0-350M is tracked, but not publicly ranked yet. The profile exposes 7 sourced benchmark rows and leaves unsupported fields blank until a published record exists.

Email me when its price, rank or availability changes.

Capability

Unranked

field median 49.4Not eligible for a public rank

Price

Self-hosted; infrastructure cost varies

input median $0.60No comparable first-party hosted token rate

Speed

Not measured

field median 92 tok/sTime to first token not measured

Context

32Ktokens

field median 256,000Reported for this model; direct source link not stored

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.

Strongest published evidence

Instruction Following ranks #147. A well-rounded choice across a range of tasks.

Validate before choosing

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

Source-linked · 7 displayable benchmark rows

Is Granite-4.0-350M right for your job?

Its place on the LLM Selector’s job rankings, the same order the job pages publish. Describe your own job to see where it stands for that.

Where Granite-4.0-350M ranks by benchmark score:

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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 measured
Not measured
CodingWeight 20%1 benchmarkVerified
Score pending
ReasoningRank Not rankedWeight 17%1 benchmarkVerified
23.3
MultimodalWeight 12%0 benchmarksNot measured
Not measured
KnowledgeRank Not rankedWeight 12%3 benchmarksVerified
19.7
MultilingualWeight 7%1 benchmarkVerified
Score pending
Inst. FollowingRank #147 of 157Percentile 6thWeight 5%1 benchmarkVerified
28.8
MathWeight 5%0 benchmarksNot measured
Not measured

7 of 906 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

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. Coding1/1 verified
  3. Reasoning1/1 verified
  4. MultimodalNot measured
  5. Knowledge3/3 verified
  6. Multilingual1/1 verified
  7. Inst. Following1/1 verified
  8. MathNot measured
Verified sourceProvisionalNot measured

Benchmark ledger

Every result says who ran the test. For this model, IBM ran all 7. Coding opens by default. The tick marks the best source-verified result in the catalog; provisional leaders do not set the reference.

Overall rank
Not ranked yet
No benchmark-run or independent results yet
Reported by IBM
7
Shown with that label
Run by a benchmark
0
None yet
Independent runs
0
None yet
Coding1 row
Coding benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
HumanEvalEvaluating Large Language Models Trained on Code39%

Best verified: o4-mini · 97.3%

Gap58.3 behindReported by IBMShown only
Reasoning1 row
Reasoning benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
BBHBIG-Bench Hard33.1%

Best verified: Claude 3.5 Haiku · 86.6%

Gap53.5 behindReported by IBMShown only
Knowledge3 rows
Knowledge benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
MMLU-ProMassive Multitask Language Understanding Professional12.1%

Best verified: Qwen3.7 Max · 89.6%

Gap77.5 behindReported by IBMWeighted benchmark6% ref. weight
GPQAGraduate-Level Google-Proof Q&A24.1%

Best verified: GPT-6 Astra · 96%

Gap71.9 behindReported by IBMWeighted benchmark3% ref. weight
MMLUMassive Multitask Language Understanding35.0%

Best verified: o1 · 91.8%

Gap56.8 behindReported by IBMShown only
Multilingual1 row
Multilingual benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
MGSMMultilingual Grade School Math14.7%

Best verified: o4-mini · 93.7%

Gap79 behindReported by IBMShown only
Inst. Following1 row
Inst. Following benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
IFEvalInstruction-Following Eval55.4%

Best verified: LFM2.5-24B-A2B · 96.8%

Gap41.4 behindReported by IBMShown only

Outlined bars are lab-reported. Filled bars are benchmark-run or independent. Bars run 0–100; the dark tick marks the best source-verified value.

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Oct 28, 2025 · you are here

    Granite-4.0-350M

    Not publicly ranked · Price not listed

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
Not published
Context window
32K
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
Not sourced yet
Cloud regions
Not tracked yet
Lifecycle
Established
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing record
Self-host
Open weights available; hardware estimate not sourced
Rate limits
Not tracked yet

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 Granite-4.0-350M, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.

Granite-4.0-350M is a open weight model with a 32K context window. No explicit reasoning mode is documented in this profile.

Official exact-value snapshot from IBM's Granite 4.0 Nano Language Models model card table. BenchLM maps the directly comparable MMLU, MMLU-Pro, GPQA, IFEval Average, BBH, HumanEval, and MGSM results published for the 350M dense instruct model.

Granite-4.0-350M sits in the Granite 4.0 350M family with Granite-4.0-H-350M. 7 of 906 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Instruction Following at #147. a well-rounded choice across a range of tasks.

Last updated November 12, 2026. Runtime fields remain blank until a sourced snapshot exists.

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

Questions

How does Granite-4.0-350M perform overall in AI benchmarks?

Granite-4.0-350M has 7 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 Granite-4.0-350M good for knowledge and understanding?

Granite-4.0-350M has source-displayable benchmark coverage for knowledge and understanding, 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 Granite-4.0-350M good for coding and programming?

Granite-4.0-350M has source-displayable benchmark coverage for coding and programming, 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 Granite-4.0-350M good for reasoning and logic?

Granite-4.0-350M has source-displayable benchmark coverage for reasoning and logic, 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 Granite-4.0-350M good for instruction following?

Granite-4.0-350M ranks #147 out of 157 eligible models for instruction following, with a public category score of 28.8/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Granite-4.0-350M good for multilingual tasks?

Granite-4.0-350M has source-displayable benchmark coverage for multilingual 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 Granite-4.0-350M open source?

Granite-4.0-350M is an open-weight model from IBM. 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.

Which sibling models are related to Granite-4.0-350M?

Granite-4.0-350M belongs to the Granite 4.0 350M family. Related tracked variants include Granite-4.0-H-350M. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

Does Granite-4.0-350M have full benchmark coverage on BenchLM?

No. Granite-4.0-350M currently has 13 source-displayable rows. 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 Granite-4.0-350M?

Granite-4.0-350M has a reported context window of 32K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.

Compare Granite-4.0-350M with every tracked model1030 comparisons