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Llama 3.3 70B Instruct vs Nex-N2-Pro

Detailed technical comparison between Llama 3.3 70B Instruct (Meta) and Nex-N2-Pro (Nex AGI). Review live API token pricing, context window capabilities, time-to-first-token latency, and verified benchmark scores side-by-side.

⚡ Executive Summary & Verdict

Comparison Snapshot

Llama 3.3 70B Instruct: 5 WinsvsNex-N2-Pro: 1 Win
Context Leader

Nex-N2-Pro

262,144 tokens
Agentic Tool-Calling

Tie

Equal Capability
Lowest Latency (TTFT)

Tie

Equal Speed
Input Price Leader

Llama 3.3 70B Instruct

$0.13 / MTok
Metaactive

Llama 3.3 70B Instruct

Meta's state-of-the-art open weights model, providing enterprise-grade reasoning and logic. Exceptionally powerful for self-hosted customer support, text generation, and tooling workflows.

View Llama 3.3 70B Instruct Full Specs →
Nex AGIactive

Nex-N2-Pro

Nex-N2-Pro is an agentic mixture-of-experts model from Nex AGI, with 17B active parameters out of 397B total. Built on the Qwen3.5 architecture, it accepts text and image input and produces...

View Nex-N2-Pro Full Specs →

Technical Specifications

🏆 = Superior Spec
SpecificationLlama 3.3 70B InstructNex-N2-Pro
ProviderMetaNex AGI
Context Window131,072 tokens262,144 tokens🏆
Agent Suitability83/100 (est.)Not yet benchmarked
Time to First Token (TTFT)280 ms (est.)No public TTFT data
Deployment Modelself hostablemanaged api
Production StabilityStable GA (est.)Stable GA (est.)
API AvailableYesYes
Released Date2024-12-062026-06-08

API Pricing Comparison

Input Price per Million Tokens

Llama 3.3 70B Instruct

$0.13

Nex-N2-Pro

$0.25

Output Price per Million Tokens

Llama 3.3 70B Instruct

$0.40

Nex-N2-Pro

$1.00

💡 Cost Ratio: Llama 3.3 70B Instruct is 1.9x cheaper per input token than Nex-N2-Pro.

Want to test both models live?

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Benchmark Performance Metrics

Standardized Scores (0–100%)

Scores show verified raw accuracy percentages across standardized AI evaluation suites. Higher bars indicate superior performance in that domain.

MMLUGeneral knowledge & multi-task understanding
86.2%vs85.6%+0.6% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
Nex-N2-Pro
HumanEvalPython coding & logic synthesis
88.0%vs84.8%+3.2% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
Nex-N2-Pro
MATHComplex mathematical problem solving
75.0%vs66.0%+9.0% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
Nex-N2-Pro
GPQAGraduate-level expert reasoning
52.0%vs47.4%+4.6% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
Nex-N2-Pro
HellaSwagCommonsense reasoning and inference
88.5%vs86.6%+1.9% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
Nex-N2-Pro
MT-BenchMulti-turn conversation flow quality
8.8%vs8.9%+0.1% Nex-N2-Pro
Llama 3.3 70B Instruct
Nex-N2-Pro 🏆

Llama 3.3 70B Instruct Quirks & Gotchas

  • ▸Stable, well-documented self-hosted option with strong community support
  • ▸Outperformed by Llama 4 Maverick for agentic tool-calling workflows

Nex-N2-Pro Quirks & Gotchas

No developer gotchas reported.

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