Model comparison

Ling 3.1 Flash vs GPT-6.1 Sol

inclusionAI's Ling 3.1 Flash against OpenAI's GPT-6.1 Sol: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.

CheaperLing 3.1 Flash

Ling 3.1 Flash lists at $0.112 per million tokens blended against $4.00 for GPT-6.1 Sol - 36x less.

Stronger on intelligenceGPT-6.1 Sol

GPT-6.1 Sol scores 55 on the intelligence index against 41 - 13 points apart.

Longer contextGPT-6.1 Sol

GPT-6.1 Sol reads 1.1M tokens at once, Ling 3.1 Flash 1M.

Abilities differGPT-6.1 Sol

only GPT-6.1 Sol offers image input.

Side by side

Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).

Specification	Ling 3.1 Flash	GPT-6.1 Sol
Input price / 1M tokens	$0.07	$2.00
Output price / 1M tokens	$0.23	$10.0
Blended (3 in : 1 out)	$0.11	$4.00
Cached input / 1M	$0.01	$0.10
Context window	1M	1.1M
Max output	262K	128K
Input	text	file, image, text
Output	text	text
Tool calls
Structured output
Reasoning
Open weights
Intelligence index	41	55
Coding index	-	-
Agentic index	-	-
Released	Oct 2, 2026	Sep 29, 2026
Available on this deployment

What a month costs

List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.

Workload	Ling 3.1 Flash	GPT-6.1 Sol	Difference
Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations.	$2.63	$90.00	$87.38 less on Ling 3.1 Flash
Document summaries100M in, 5M out - long inputs, short answers.	$8.63	$250	$241 less on Ling 3.1 Flash
Coding assistant60M in, 20M out - a team's editor assistant, output-heavy.	$9.00	$320	$311 less on Ling 3.1 Flash

What the router does with the pair

Ling 3.1 Flash takes the short and general requests - at $0.112 blended it is the default - and GPT-6.1 Sol the ones whose complexity or quality floor rules the cheaper model out. The split is per request, not per app, and every decision carries the scores for both.

Price a request Playground

yaml

# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
  - { id: ling-3.1-flash, kind: llm, metadata: { model: inclusionai/ling-3.1-flash } }
  - { id: gpt-6.1-sol, kind: llm, metadata: { model: openai/gpt-6.1-sol } }

# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
  - name: simple-requests-stay-cheap
    when: { max_complexity: 0.45 }
    prefer: [ling-3.1-flash]
    weight: 0.8
  - name: hard-requests-escalate
    when: { min_complexity: 0.45 }
    prefer: [gpt-6.1-sol]
    weight: 0.8

Frequently asked

Is Ling 3.1 Flash cheaper than GPT-6.1 Sol?: Yes. Ling 3.1 Flash lists at $0.07 in / $0.23 out per million tokens, GPT-6.1 Sol at $2.00 in / $10.0 out; blended, Ling 3.1 Flash is the cheaper one.
Which is better, Ling 3.1 Flash or GPT-6.1 Sol?: On the Artificial Analysis intelligence index GPT-6.1 Sol scores higher (55 against 41). "Better" depends on the task: the coding and agentic indexes below and the abilities row may point the other way for your requests.
Which has the longer context window?: GPT-6.1 Sol - 1.1M tokens against 1M.
Can I use Ling 3.1 Flash and GPT-6.1 Sol together?: Yes. Declare both as targets and OpenSmartRoute picks one per request on quality for the task, price and your rules - the cheaper model for the requests it can answer, the stronger one for the rest - and records why in the trace.

Also compared

Every inclusionAI and OpenAI model, with prices, is on the vendor pages; the rankings show what the router actually picks.

For the business

What it means for which models you pay for.

Catalogue of every vendor
The right model, not the famous one
Price, context, abilities and benchmark indexes side by side, with the model that fits each task named.
The rankings
89% less
Lower cost per request
On the 150-prompt benchmark the router spent 89% less than sending every prompt to the reference model, and the judged quality went up, not down.
Read the benchmark
Per million tokens, every model
Prices you can compare
The catalogue prints input and output prices and what three typical monthly workloads cost on each.
The model catalogue

Which LLM is the cheapest for our workload?: The best-for pages rank the live catalogue per task by the index that measures it, and the router picks the cheapest model that meets your quality rule for each request.
How much can we save on our LLM bill?: On the public benchmark the router saved 89% against sending everything to the reference model. Import last month's provider bill on the bill page and the estimate is computed on your own traffic before you change a line of code.
How often do model prices change?: The catalogue is refreshed from the vendors' price lists; each model page shows the date of its data.

Stop choosing between Ling 3.1 Flash and GPT-6.1 Sol. Route to both.

Declare the pair as targets and OpenSmartRoute picks per request - the cheaper one where it holds up, the stronger one where it matters - with the comparison in every trace.

Create a free account Try a request

Free plan, no card. Fifteen thousand decisions a month with the full trace.

Specification

Ling 3.1 Flash

GPT-6.1 Sol

Input price / 1M tokens

$0.07

$2.00

Output price / 1M tokens

$0.23

$10.0

Blended (3 in : 1 out)

$0.11

$4.00

Cached input / 1M

$0.01

$0.10

Context window

1.1M

Max output

262K

128K

Input

text

file, image, text

Output

text

Tool calls

Structured output

Reasoning

Open weights

Intelligence index

Coding index

Agentic index

Released

Oct 2, 2026

Sep 29, 2026

Available on this deployment

Workload

Ling 3.1 Flash

GPT-6.1 Sol

Difference

Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations.

$2.63

$90.00

$87.38 less on Ling 3.1 Flash

Document summaries100M in, 5M out - long inputs, short answers.

$8.63

$250

$241 less on Ling 3.1 Flash

Coding assistant60M in, 20M out - a team's editor assistant, output-heavy.

$9.00

$320

$311 less on Ling 3.1 Flash

# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry) targets: - { id: ling-3.1-flash, kind: llm, metadata: { model: inclusionai/ling-3.1-flash } } - { id: gpt-6.1-sol, kind: llm, metadata: { model: openai/gpt-6.1-sol } } # rules.yaml - the cheaper model by default, the stronger one when the request is hard rules: - name: simple-requests-stay-cheap when: { max_complexity: 0.45 } prefer: [ling-3.1-flash] weight: 0.8 - name: hard-requests-escalate when: { min_complexity: 0.45 } prefer: [gpt-6.1-sol] weight: 0.8

Frequently asked

Is Ling 3.1 Flash cheaper than GPT-6.1 Sol?

Yes. Ling 3.1 Flash lists at $0.07 in / $0.23 out per million tokens, GPT-6.1 Sol at $2.00 in / $10.0 out; blended, Ling 3.1 Flash is the cheaper one.

Which is better, Ling 3.1 Flash or GPT-6.1 Sol?

On the Artificial Analysis intelligence index GPT-6.1 Sol scores higher (55 against 41). "Better" depends on the task: the coding and agentic indexes below and the abilities row may point the other way for your requests.

Which has the longer context window?

GPT-6.1 Sol - 1.1M tokens against 1M.

Can I use Ling 3.1 Flash and GPT-6.1 Sol together?

Yes. Declare both as targets and OpenSmartRoute picks one per request on quality for the task, price and your rules - the cheaper model for the requests it can answer, the stronger one for the rest - and records why in the trace.

Ling 3.1 Flash vs GPT-6.1 Sol

Side by side

What a month costs

What the router does with the pair

Frequently asked

Also compared

What it means for which models you pay for.

The right model, not the famous one

Lower cost per request

Prices you can compare

Stop choosing between Ling 3.1 Flash and GPT-6.1 Sol. Route to both.

Ling 3.1 Flash vs GPT-6.1 Sol

Side by side

What a month costs

What the router does with the pair

Frequently asked

Also compared

What it means for which models you pay for.

The right model, not the famous one

Lower cost per request

Prices you can compare

Stop choosing between Ling 3.1 Flash and GPT-6.1 Sol. Route to both.