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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

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 →

Technical Specifications

🏆 = Superior Spec
SpecificationLaguna M.1Llama 3.3 70B Instruct
ProviderPoolsideMeta
Context Window262,144 tokens🏆131,072 tokens
Agent SuitabilityNot yet benchmarked83/100 (est.)
Time to First Token (TTFT)No public TTFT data280 ms (est.)
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?

Run side-by-side prompt benchmarks in our dynamic multi-model Sandbox. Compare execution speeds, latency metrics, and compute actual costs in real-time.

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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%vs86.2%+5.0% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
HumanEvalPython coding & logic synthesis
79.4%vs88.0%+8.6% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
MATHComplex mathematical problem solving
56.6%vs75.0%+18.4% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
GPQAGraduate-level expert reasoning
38.6%vs52.0%+13.4% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
HellaSwagCommonsense reasoning and inference
80.8%vs88.5%+7.7% Llama 3.3 70B Instruct
Laguna M.1
Llama 3.3 70B Instruct 🏆
MT-BenchMulti-turn conversation flow quality
8.3%vs8.8%+0.5% 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

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

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