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Laguna M.1 vs DeepSeek V3.1

Detailed technical comparison between Laguna M.1 (Poolside) and DeepSeek V3.1 (DeepSeek). Review live API token pricing, context window capabilities, time-to-first-token latency, and verified benchmark scores side-by-side.

โšก Executive Summary & Verdict

Comparison Snapshot

Laguna M.1: 3 WinsvsDeepSeek V3.1: 3 Wins
Context Leader

Laguna M.1

262,144 tokens
Agentic Tool-Calling

Tie

Equal Capability
Lowest Latency (TTFT)

Tie

Equal Speed
Input Price Leader

Laguna M.1

$0.20 / 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 โ†’
DeepSeekactive

DeepSeek V3.1

DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context...

View DeepSeek V3.1 Full Specs โ†’

Technical Specifications

๐Ÿ† = Superior Spec
SpecificationLaguna M.1DeepSeek V3.1
ProviderPoolsideDeepSeek
Context Window262,144 tokens๐Ÿ†163,840 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 Date2026-04-282025-08-21

API Pricing Comparison

Input Price per Million Tokens

Laguna M.1

$0.20

DeepSeek V3.1

$0.25

Output Price per Million Tokens

Laguna M.1

$0.40

DeepSeek V3.1

$0.95

๐Ÿ’ก Cost Ratio: Laguna M.1 is 1.3x cheaper per input token than DeepSeek V3.1.

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.

Benchmark Performance Metrics

Standardized Scores (0โ€“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%vs80.0%+1.2% Laguna M.1
Laguna M.1 ๐Ÿ†
DeepSeek V3.1
HumanEvalPython coding & logic synthesis
79.4%vs78.2%+1.2% Laguna M.1
Laguna M.1 ๐Ÿ†
DeepSeek V3.1
MATHComplex mathematical problem solving
56.6%vs55.4%+1.2% Laguna M.1
Laguna M.1 ๐Ÿ†
DeepSeek V3.1
GPQAGraduate-level expert reasoning
38.6%vs40.8%+2.2% DeepSeek V3.1
Laguna M.1
DeepSeek V3.1 ๐Ÿ†
HellaSwagCommonsense reasoning and inference
80.8%vs83.0%+2.2% DeepSeek V3.1
Laguna M.1
DeepSeek V3.1 ๐Ÿ†
MT-BenchMulti-turn conversation flow quality
8.3%vs8.6%+0.2% DeepSeek V3.1
Laguna M.1
DeepSeek V3.1 ๐Ÿ†

Laguna M.1 Quirks & Gotchas

No developer gotchas reported.

DeepSeek V3.1 Quirks & Gotchas

No developer gotchas reported.

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