Pairwise benchmark snapshot · Aug 16, 2026noindex (thin pair)

Thinking Machines: Inkling (batch) vs Mistral Large 3 benchmark

Name: Thinking Machines: Inkling (batch) vs Mistral Large 3 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Thinking Machines: Inkling (batch) (thinkingmachines) and Mistral Large 3 (Mistral) using the latest snapshots we have as of Aug 16, 2026. Thinking Machines: Inkling (batch) wins on output price at $2.03/1M. Thinking Machines: Inkling (batch) has the larger context window (524k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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thinkingmachines

Thinking Machines: Inkling (batch)

Auto-discovered from OpenRouter (thinkingmachines/inkling:batch). Preview until a second source matches.

$2.025/1M out

Mistral

Mistral Large 3

Mistral European flagship with strong function calling.

Elo 1,456$6/1M out

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Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Thinking Machines: Inkling (batch) (3)vsMistral Large 3 (6)

Thinking Machines: Inkling (batch): 3W (33%)Overall: Mistral Large 3Mistral Large 3: 6W (67%)

← Thinking Machines: Inkling (batch)Mistral Large 3 →

Mistral Large 3 (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,456

Coding Elo

—vs—

SWE-bench

—vs55.4%

LiveBench

—vs61.8%

GPQA Diamond

—vs74.1%

Thinking Machines: Inkling (batch): 0WMistral Large 3: 4W

Mistral Large 3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs112 tok/s

Time to first token

—vs190 ms

Thinking Machines: Inkling (batch): 0WMistral Large 3: 2W

Thinking Machines: Inkling (batch) (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.03/1Mvs$6/1M

Input price

$0.5/1Mvs$2/1M

Context window

524kvs128k

Thinking Machines: Inkling (batch): 3WMistral Large 3: 0W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageMistral Large 3 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Thinking Machines: Inkling (batch)

$3.98/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,456

LiveBench

High is better

—

LiveBenchHigh is better

61.8%

SWE-bench

High is better

—

SWE-benchHigh is better

55.4%

GPQA

High is better

—

GPQAHigh is better

74.1%

TTFT

Low is better

—

TTFTLow is better

190 ms

Speed

High is better

—

SpeedHigh is better

112 tok/s

In $

Low is better

+300%$0.5/1M

In $Low is better

$2/1M

Out $

Low is better

+196%$2.03/1M

Out $Low is better

$6/1M

Context

High is better

+310%524k

ContextHigh is better

128k

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 70% with Thinking Machines: Inkling (batch)

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

Thinking Machines: Inkling (batch)$47.88 / mo

In: $17.50Out: $30.38

Mistral Large 3$160.00 / mo

In: $70.00Out: $90.00

Estimated Cost Delta

Thinking Machines: Inkling (batch) is estimated to save $112.13/month ($1,346/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Thinking Machines: Inkling (batch):Pick Thinking Machines: Inkling (batch) when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatThinking Machines: Inkling (batch)

Lower output list price ($2.03/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGThinking Machines: Inkling (batch)

Larger window (524k).

Screenshots / visionMistral Large 3

Mistral Large 3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Thinking Machines: Inkling (batch)	Lower output list price ($2.03/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Thinking Machines: Inkling (batch)	Larger window (524k).
Screenshots / vision	Mistral Large 3	Mistral Large 3 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchJan 18, 2026

Mistral Large 3: the European flagship with function calling

Jan 2026. Not the Elo crown. Useful when residency and tools matter more than Arena.

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LIVE

thinkingmachines

Thinking Machines: Inkling (batch)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$2.03/1M

Mistral

Mistral Large 3

Elo 1,456

LiveBench61.8%

SWE-bench55.4%

Speed112 tok/s

Output cost$6/1M

Frequently Asked Questions

Which is better overall, Thinking Machines: Inkling (batch) or Mistral Large 3?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, Thinking Machines: Inkling (batch) or Mistral Large 3?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: Thinking Machines: Inkling (batch) output tokens are $2.03/1M versus $6/1M. Input prices and retry rates still move the real bill.
Which is faster, Thinking Machines: Inkling (batch) or Mistral Large 3?: We do not have TTFT for both models.
Which has the larger context window?: Thinking Machines: Inkling (batch) accepts 524k tokens versus 128k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Thinking Machines: Inkling (batch) and Mistral Large 3 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Thinking Machines: Inkling (batch) matchups →|All Mistral Large 3 matchups →

Mistral Large 3 vs Codestral 25.01 (previous mistral)Claude Opus 4.5 vs Mistral Large 3 Claude Opus 4.6 vs Mistral Large 3 Claude Sonnet 4.5 vs Mistral Large 3 GPT-5 vs Mistral Large 3 Gemini 3 Pro vs Mistral Large 3

Thinking Machines: Inkling (batch) vs Mistral Large 3 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Thinking Machines: Inkling (batch) (3)vsMistral Large 3 (6)

Thinking Machines: Inkling (batch): 3W (33%)Overall: Mistral Large 3Mistral Large 3: 6W (67%)

← Thinking Machines: Inkling (batch)Mistral Large 3 →

Mistral Large 3 (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,456

Coding Elo

—vs—

SWE-bench

—vs55.4%

LiveBench

—vs61.8%

GPQA Diamond

—vs74.1%

Thinking Machines: Inkling (batch): 0WMistral Large 3: 4W

Mistral Large 3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs112 tok/s

Time to first token

—vs190 ms

Thinking Machines: Inkling (batch): 0WMistral Large 3: 2W

Thinking Machines: Inkling (batch) (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.03/1Mvs$6/1M

Input price

$0.5/1Mvs$2/1M

Context window

524kvs128k

Thinking Machines: Inkling (batch): 3WMistral Large 3: 0W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatThinking Machines: Inkling (batch)

Lower output list price ($2.03/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGThinking Machines: Inkling (batch)

Larger window (524k).

Screenshots / visionMistral Large 3

Mistral Large 3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Thinking Machines: Inkling (batch)	Lower output list price ($2.03/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Thinking Machines: Inkling (batch)	Larger window (524k).
Screenshots / vision	Mistral Large 3	Mistral Large 3 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Thinking Machines: Inkling (batch) or Mistral Large 3?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, Thinking Machines: Inkling (batch) or Mistral Large 3?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

Thinking Machines: Inkling (batch) output tokens are $2.03/1M versus $6/1M. Input prices and retry rates still move the real bill.

Which is faster, Thinking Machines: Inkling (batch) or Mistral Large 3?

We do not have TTFT for both models.

Which has the larger context window?

Thinking Machines: Inkling (batch) accepts 524k tokens versus 128k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Thinking Machines: Inkling (batch) and Mistral Large 3 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

Thinking Machines: Inkling (batch)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Mistral Large 3: the European flagship with function calling

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

Thinking Machines: Inkling (batch)

Mistral Large 3

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

Thinking Machines: Inkling (batch)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Mistral Large 3: the European flagship with function calling

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Thinking Machines: Inkling (batch)

Mistral Large 3

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons