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

Codestral 25.01 vs inclusionAI: Ling-2.6-flash benchmark

Name: Codestral 25.01 vs inclusionAI: Ling-2.6-flash AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Codestral 25.01 (Mistral) and inclusionAI: Ling-2.6-flash (inclusionai) using the latest snapshots we have as of Aug 16, 2026. inclusionAI: Ling-2.6-flash wins on output price at $0.03/1M. inclusionAI: Ling-2.6-flash has the larger context window (262k 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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Mistral

Codestral 25.01

Mistral code-specialist model.

Elo 1,320$0.9/1M out

inclusionai

inclusionAI: Ling-2.6-flash

Auto-discovered from OpenRouter (inclusionai/ling-2.6-flash). Preview until a second source matches.

$0.03/1M out

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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.

Codestral 25.01 (7)vsinclusionAI: Ling-2.6-flash (3)

Codestral 25.01: 7W (70%)Overall: Codestral 25.01inclusionAI: Ling-2.6-flash: 3W (30%)

← Codestral 25.01inclusionAI: Ling-2.6-flash →

Codestral 25.01 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,320vs—

Coding Elo

1,402vs—

SWE-bench

51.8%vs—

LiveBench

54%vs—

GPQA Diamond

58.4%vs—

Codestral 25.01: 5WinclusionAI: Ling-2.6-flash: 0W

Codestral 25.01 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

148 tok/svs—

Time to first token

150 msvs—

Codestral 25.01: 2WinclusionAI: Ling-2.6-flash: 0W

inclusionAI: Ling-2.6-flash (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.9/1Mvs$0.03/1M

Input price

$0.3/1Mvs$0.01/1M

Context window

256kvs262k

Codestral 25.01: 0WinclusionAI: Ling-2.6-flash: 3W

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 AdvantageCodestral 25.01 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

inclusionAI: Ling-2.6-flash

$0.87/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,320

EloHigh is better

—

Code Elo

High is better

1,402

Code EloHigh is better

—

LiveBench

High is better

54%

LiveBenchHigh is better

—

SWE-bench

High is better

51.8%

SWE-benchHigh is better

—

GPQA

High is better

58.4%

GPQAHigh is better

—

TTFT

Low is better

150 ms

TTFTLow is better

—

Speed

High is better

148 tok/s

SpeedHigh is better

—

In $

Low is better

$0.3/1M

In $Low is better

$0.01/1M+2900%

Out $

Low is better

$0.9/1M

Out $Low is better

$0.03/1M+2900%

Context

High is better

256k

ContextHigh is better

262k+2%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 97% with inclusionAI: Ling-2.6-flash

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Codestral 25.01$24.00 / mo

In: $10.50Out: $13.50

inclusionAI: Ling-2.6-flash$0.80 / mo

In: $0.35Out: $0.45

Estimated Cost Delta

inclusionAI: Ling-2.6-flash is estimated to save $23.20/month ($278/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
inclusionAI: Ling-2.6-flash:Pick inclusionAI: Ling-2.6-flash when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatinclusionAI: Ling-2.6-flash

Lower output list price ($0.03/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGinclusionAI: Ling-2.6-flash

Larger window (262k).

Screenshots / visioninclusionAI: Ling-2.6-flash

inclusionAI: Ling-2.6-flash 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	inclusionAI: Ling-2.6-flash	Lower output list price ($0.03/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	inclusionAI: Ling-2.6-flash	Larger window (262k).
Screenshots / vision	inclusionAI: Ling-2.6-flash	inclusionAI: Ling-2.6-flash is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchJan 14, 2025

Codestral 25.01: Mistral’s code-specialist row

Jan 2025 code model. Still a valid cheap-coding compare. Not a chatbot.

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LIVE

Mistral

Codestral 25.01

Elo 1,320

LiveBench54%

SWE-bench51.8%

Speed148 tok/s

Output cost$0.9/1M

inclusionai

inclusionAI: Ling-2.6-flash

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.03/1M

Frequently Asked Questions

Which is better overall, Codestral 25.01 or inclusionAI: Ling-2.6-flash?: 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, Codestral 25.01 or inclusionAI: Ling-2.6-flash?: 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?: inclusionAI: Ling-2.6-flash output tokens are $0.03/1M versus $0.9/1M. Input prices and retry rates still move the real bill.
Which is faster, Codestral 25.01 or inclusionAI: Ling-2.6-flash?: We do not have TTFT for both models.
Which has the larger context window?: inclusionAI: Ling-2.6-flash accepts 262k tokens versus 256k.
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. Codestral 25.01 and inclusionAI: Ling-2.6-flash Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Codestral 25.01 matchups →|All inclusionAI: Ling-2.6-flash matchups →

Codestral 25.01 vs Mistral Large 3 (next mistral)Doubao Pro 1.5 vs Codestral 25.01 Yi-Lightning vs Codestral 25.01 Claude Opus 4.5 vs Codestral 25.01 Claude Opus 4.6 vs Codestral 25.01 Claude Sonnet 4.5 vs Codestral 25.01

Codestral 25.01 vs inclusionAI: Ling-2.6-flash benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Codestral 25.01 (7)vsinclusionAI: Ling-2.6-flash (3)

Codestral 25.01: 7W (70%)Overall: Codestral 25.01inclusionAI: Ling-2.6-flash: 3W (30%)

← Codestral 25.01inclusionAI: Ling-2.6-flash →

Codestral 25.01 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,320vs—

Coding Elo

1,402vs—

SWE-bench

51.8%vs—

LiveBench

54%vs—

GPQA Diamond

58.4%vs—

Codestral 25.01: 5WinclusionAI: Ling-2.6-flash: 0W

Codestral 25.01 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

148 tok/svs—

Time to first token

150 msvs—

Codestral 25.01: 2WinclusionAI: Ling-2.6-flash: 0W

inclusionAI: Ling-2.6-flash (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.9/1Mvs$0.03/1M

Input price

$0.3/1Mvs$0.01/1M

Context window

256kvs262k

Codestral 25.01: 0WinclusionAI: Ling-2.6-flash: 3W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatinclusionAI: Ling-2.6-flash

Lower output list price ($0.03/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGinclusionAI: Ling-2.6-flash

Larger window (262k).

Screenshots / visioninclusionAI: Ling-2.6-flash

inclusionAI: Ling-2.6-flash 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	inclusionAI: Ling-2.6-flash	Lower output list price ($0.03/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	inclusionAI: Ling-2.6-flash	Larger window (262k).
Screenshots / vision	inclusionAI: Ling-2.6-flash	inclusionAI: Ling-2.6-flash is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Codestral 25.01 or inclusionAI: Ling-2.6-flash?

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, Codestral 25.01 or inclusionAI: Ling-2.6-flash?

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?

inclusionAI: Ling-2.6-flash output tokens are $0.03/1M versus $0.9/1M. Input prices and retry rates still move the real bill.

Which is faster, Codestral 25.01 or inclusionAI: Ling-2.6-flash?

We do not have TTFT for both models.

Which has the larger context window?

inclusionAI: Ling-2.6-flash accepts 262k tokens versus 256k.

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. Codestral 25.01 and inclusionAI: Ling-2.6-flash 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

inclusionAI: Ling-2.6-flash

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Codestral 25.01: Mistral’s code-specialist row

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

Codestral 25.01

inclusionAI: Ling-2.6-flash

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

inclusionAI: Ling-2.6-flash

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Codestral 25.01: Mistral’s code-specialist row

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Codestral 25.01

inclusionAI: Ling-2.6-flash

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons