Llama 3.1 8B vs Grok 4.3

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#40 of 4616.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 11.1–44.4
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#7 of 44$0.028 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5UnrankedNot in the 38-model eligible cohort#33 of 3846.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 43.3–59.9
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$1.25Xai · Sep 3, 2026
Output priceFrom · USD / 1M tokensNot reported$2.50Xai · Sep 3, 2026
Context windowMaximum documented tokens131K1,000K
Model facts checkedAug 28, 2026View model evidence →Sep 3, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldLlama-3.1-8BGrok 4.3
DeveloperMetaxAI
FamilyLlama 3 1 8bGrok 4
ModelLlama-3.1-8BGrok 4.3
VersionLlama-3.1-8BGrok 4.3
Lifecycleactiveactive
Released2024-07-232026-06-17
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image
Output modalitiesTextText
Context window131K1,000K
Total parameters8BUnknown
Active parametersUnknownUnknown
Licensellama3.1Unknown
Open weightsYesNo
API availableYesYes
Self-hostableYesNo
Provider accessHugging Face (Standard)Xai (Standard)
Capabilitiesgenerationchat, generation, reasoning, structured_outputs, tools

Llama 3.1 8B Capabilities

generation
Serving providers1
Canonical IDmeta-llama/Llama-3.1-8B

Grok 4.3 Capabilities

chatgenerationreasoningstructured outputstools
Serving providers1
Canonical IDxai/grok-4.3

Primary Evidence

Sources and Freshness

Questions

Llama 3.1 8B vs Grok 4.3 FAQs

Is Llama 3.1 8B or Grok 4.3 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 8B and Grok 4.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 3.1 8B or Grok 4.3?+

Only Grok 4.3 has a directly sourced input price: $1.25 per million tokens. Only Grok 4.3 has a directly sourced output price: $2.50 per million tokens.

Which has a larger context window, Llama 3.1 8B or Grok 4.3?+

Grok 4.3 has the larger sourced context window. Llama 3.1 8B supports 131K and Grok 4.3 supports 1,000K.

Which performs better in benchmarks, Llama 3.1 8B or Grok 4.3?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Llama 3.1 8B or Grok 4.3 be self-hosted?+

Llama 3.1 8B is the only model in this pair currently marked as self-hostable. Llama 3.1 8B is open weight; Grok 4.3 is not marked open weight.

Can Llama 3.1 8B and Grok 4.3 understand images?+

Llama 3.1 8B is not documented with image input; Grok 4.3 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 3.1 8B or Grok 4.3?+

Neither has a larger sourced maximum output. Llama 3.1 8B is — and Grok 4.3 is —.

Do Llama 3.1 8B and Grok 4.3 support reasoning and tool use?+

Llama 3.1 8B: none of these features are definitively sourced. Grok 4.3: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 3.1 8B or Grok 4.3?+

Llama 3.1 8B has 1 sourced provider route; Grok 4.3 has 1, a tie.

Which offers better value, Llama 3.1 8B or Grok 4.3?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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