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Laguna M.1 vs Llama 3.3 70B Instruct

Detailed technical comparison between Laguna M.1 (Poolside) and Llama 3.3 70B Instruct (Meta). 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

Laguna M.1: 0 WinsvsLlama 3.3 70B Instruct: 6 Wins
Context Leader

Laguna M.1

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
Poolsideactive

Laguna M.1

Laguna M.1 is the flagship coding agent model from [Poolside](https://poolside.ai/), optimized for complex software engineering tasks. Designed for agentic coding workflows, it supports tool calling and reasoning, with a 256K...

View Laguna M.1 Full Specs →
Metaactive

Llama 3.3 70B Instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...

View Llama 3.3 70B Instruct Full Specs →

Technical Specifications

🏆 = Superior Spec
SpecificationLaguna M.1Llama 3.3 70B Instruct
ProviderPoolsideMeta
Context Window262,144 tokens🏆131,072 tokens
Agent SuitabilityNot yet benchmarkedNot yet benchmarked
Time to First Token (TTFT)No public TTFT dataNo public TTFT data
Deployment Modelmanaged apiself hostable
Production StabilityStable GA (est.)Stable GA (est.)
API AvailableYesYes
Released Date2026-04-282024-12-06

API Pricing Comparison

Input Price per Million Tokens

Laguna M.1

$0.20

Llama 3.3 70B Instruct

$0.13

Output Price per Million Tokens

Laguna M.1

$0.40

Llama 3.3 70B Instruct

$0.40

💡 Cost Ratio: Llama 3.3 70B Instruct is 1.5x cheaper per input token than Laguna M.1.

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
81.2%vs87.0%+5.8% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
HumanEvalPython coding & logic synthesis
79.4%vs86.2%+6.8% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
MATHComplex mathematical problem solving
56.6%vs67.4%+10.8% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
GPQAGraduate-level expert reasoning
38.6%vs48.8%+10.2% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
HellaSwagCommonsense reasoning and inference
80.8%vs88.0%+7.2% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
MT-BenchMulti-turn conversation flow quality
8.3%vs9.1%+0.7% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆

Laguna M.1 Quirks & Gotchas

No developer gotchas reported.

Llama 3.3 70B Instruct Quirks & Gotchas

No developer gotchas reported.

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