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Laguna S 2.1 vs Mistral Small 3

Detailed technical comparison between Laguna S 2.1 (Poolside) and Mistral Small 3 (Mistral). 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 S 2.1: 1 WinvsMistral Small 3: 5 Wins
Context Leader

Laguna S 2.1

1,048,576 tokens
Agentic Tool-Calling

Tie

Equal Capability
Lowest Latency (TTFT)

Tie

Equal Speed
Input Price Leader

Tie

Equal Pricing
Poolsideactive

Laguna S 2.1

Laguna S 2.1 is the latest coding agent model from [Poolside](<https://poolside.ai/>). Laguna S 2.1 is a 118B total parameter model with 8B active parameters, scoring 70.2% on Terminal-Bench 2.1 and...

View Laguna S 2.1 Full Specs โ†’
Mistralactive

Mistral Small 3

Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed...

View Mistral Small 3 Full Specs โ†’

Technical Specifications

๐Ÿ† = Superior Spec
SpecificationLaguna S 2.1Mistral Small 3
ProviderPoolsideMistral
Context Window1,048,576 tokens๐Ÿ†32,768 tokens
Agent SuitabilityNot yet benchmarked84/100 (est.)
Time to First Token (TTFT)No public TTFT data120 ms (est.)
Deployment Modelmanaged apimanaged api
Production StabilityBeta Access (est.)Stable GA (est.)
API AvailableYesYes
Released Date2026-07-212025-01-30

API Pricing Comparison

Input Price per Million Tokens

Laguna S 2.1

$0.10

Mistral Small 3

$0.10

Output Price per Million Tokens

Laguna S 2.1

$0.20

Mistral Small 3

$0.30

๐Ÿ’ก Cost Ratio: Both models offer identical input token pricing ($0.10 / MTok).

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
81.0%vs81.2%+0.2% Mistral Small 3
Laguna S 2.1
Mistral Small 3 ๐Ÿ†
HumanEvalPython coding & logic synthesis
79.2%vs83.0%+3.8% Mistral Small 3
Laguna S 2.1
Mistral Small 3 ๐Ÿ†
MATHComplex mathematical problem solving
56.4%vs68.0%+11.6% Mistral Small 3
Laguna S 2.1
Mistral Small 3 ๐Ÿ†
GPQAGraduate-level expert reasoning
38.4%vs45.0%+6.6% Mistral Small 3
Laguna S 2.1
Mistral Small 3 ๐Ÿ†
HellaSwagCommonsense reasoning and inference
80.6%vs85.0%+4.4% Mistral Small 3
Laguna S 2.1
Mistral Small 3 ๐Ÿ†
MT-BenchMulti-turn conversation flow quality
8.7%vs8.3%+0.4% Laguna S 2.1
Laguna S 2.1 ๐Ÿ†
Mistral Small 3

Laguna S 2.1 Quirks & Gotchas

No developer gotchas reported.

Mistral Small 3 Quirks & Gotchas

  • โ–ธFastest TTFT at lowest cost โ€” ideal for high-volume classification
  • โ–ธNot designed for complex reasoning โ€” route multi-step tasks to Mistral Large 3

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