Llama 3.1 Instruct 8B benchmarks & scores
Independent Llama 3.1 Instruct 8B AI coding benchmark results Intelligence 8, Coding 5, Agentic 1, with a Vibe Coding Index of 4.1. Compare its overall rank, token price, context window, and practical agentic workflow strengths below.
Evidence confidence
Medium confidence
Complete core scores supported by one current verified source.
Catalog capabilities
What the model supports
Suite radar
Suite scores
Source record
Source. Artificial Analysis (artificialanalysis.ai) via OpenRouter (openrouter.ai/rankings).
TheVibeFather rankings
Llama 3.1 Instruct 8B practical agentic coding scorecard
This is our algorithmic assessment of where Llama 3.1 Instruct 8B ranks for common vibe coding and agentic engineering workflows. Every stable 0–10 score is calculated consistently from the sourced Intelligence, Coding, and Agentic signals above—never from an unsourced opinion score.
Small, well-defined code changes
Precision on scoped edits, fixes, and implementation tasks.
UI and CSS iteration
Front-end implementation with iterative tool-driven refinement.
Routine debugging
Diagnosing failures and turning reasoning into correct code changes.
Repository-wide refactors
Coordinating larger edits across files while preserving intent.
Architecture decisions
Reasoning through tradeoffs, constraints, and system-level choices.
Tool use and workflow execution
Planning and completing multi-step work with tools and feedback loops.
Long autonomous coding tasks
Sustaining coherent progress across longer agentic engineering runs.
Overall VibeFather rating
Our complete Vibe Coding Index, expressed on the same 0–10 practical scale.
We start with Intelligence, Coding, Agentic benchmark evidence, and preserve missing values as unverified.
Our code-controlled algorithm blends the native 0–100 signals by workflow and expresses the result on a stable 0–10 scale. Every required input must be present.
Each workflow score is ranked against every model with comparable evidence, so positions update when the benchmark field changes.
The Vibe Coding Index is TheVibeFather’s code-controlled 0–100 composite for practical coding and agentic capability. Price and adoption are reported independently and never alter quality. Exact weighting remains proprietary, inputs, missing-data behavior, and per-category ranks are disclosed here. Read the benchmark methodology →
Recorded score history
How Llama 3.1 Instruct 8B has moved
Baseline snapshot recorded.
Where Llama 3.1 Instruct 8B sits
Its logo stays full-color and highlighted while the rest of the field recedes for quick comparison.
Alibaba
Amazon
Anthropic
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DeepSeek
Google
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Meta
Meta Llama
MiniMax
Mistral
Moonshot
NVIDIA
Nex Agi
OpenAI
Stepfun
Tencent
Upstage
Xiaomi
Zhipu
xAI
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27 model(s) hidden — data not yet verified Model benchmark FAQ
Llama 3.1 Instruct 8B for vibe coding and agentic engineering
Answers update from this model’s current sourced scores, practical rankings, and verified pricing.
What is Llama 3.1 Instruct 8B's Vibe Coding Index?
Llama 3.1 Instruct 8B has a Vibe Coding Index of 4.1/100 and is #97 of 100 in the current field. The index is TheVibeFather's stable composite for practical AI coding, combining independently sourced Intelligence, Coding, and Agentic benchmark signals. Price and adoption are compared separately and never inflate the quality score.
How does Llama 3.1 Instruct 8B rank for vibe coding?
For vibe coding, Llama 3.1 Instruct 8B's strongest practical area is Routine debugging at 0.7/10, ranking #97 of 100 models in TheVibeFather rankings.
Is Llama 3.1 Instruct 8B good for agentic coding and autonomous tasks?
Llama 3.1 Instruct 8B scores 0.2/10 for tool use and workflow execution and 0.2/10 for long autonomous coding tasks. Those categories emphasize the Agentic benchmark signal used for multi-step planning, tool calls, edits, tests, and feedback loops.
What coding benchmark scores does Llama 3.1 Instruct 8B have?
The current sourced benchmark profile for Llama 3.1 Instruct 8B is Intelligence 8/100, Coding 5/100, Agentic 1/100. TheVibeFather converts those 0–100 signals into consistent practical 0–10 workflow scores so models can be compared for real agentic engineering work.
How much does Llama 3.1 Instruct 8B cost for agentic engineering?
The current benchmark uses a 3 to 1 input-to-output blend of $0.03 per million blended tokens. That places it #4 of 105 by price, where a lower rank number means less expensive.
Are vibe coding, agentic coding, and agentic engineering the same?
They describe overlapping AI-assisted software workflows. Vibe coding emphasizes directing software through intent and iteration, agentic coding emphasizes a model planning and executing multi-step work with tools, agentic engineering is the broader discipline of designing, supervising, and validating those workflows. TheVibeFather benchmarks the shared capabilities behind all three terms on every model page, including Llama 3.1 Instruct 8B.