Model index · Text · text generation · standard · open weights · inclusionAI · released 2026-04-21

Ling-2.6-flash pricing

Served by 2 providers from $0.0150 to $0.1500 per 1M tokens on the standard tier — a 10.0× gap for identical weights.

Our two sources disagree on this price

OpenRouter quotes $0.0150 per 1M while Novita, the cheapest host we hold for it, publishes $0.1500 — 10.0x apart. Both come from the seller's own API, so one is wrong and we cannot tell which. Held out of price rankings until they agree.

Every row below carries the source it came from, so you can see both figures and check them. We would rather show you the disagreement than pick a side and be confidently wrong.

Ling-2.6-flash is an instant (instruct) model from inclusionAI with 104B total parameters and 7.4B active parameters, designed for real-world agents that require fast responses, strong execution, and high token efficiency....

inclusionAI · 107.49B parameters · mit · first published 2026-04-21 · Text model · text generation · 262k context · description as published by the source, not written by us

Open weights: inclusionAI/Ling-2.6-flash · 107.49B parameters counted from the weight files · licence mit · 0.0M downloads in the last 30 days . Parameter count and licence come from the public weights registry, not from a seller — and the download figure exists only for models published this way, so it is not a market-wide popularity measure.

$0.0150

Cheapest standard /1M — OpenRouter (default route)

262k

Native context window

2

Providers serving it

40

Measured quality /100 · mid

Measured quality

Our own measurement (text-v1 v1), run 2026-08-13 at temperature 0 and scored by deterministic checks — no LLM judge, no vendor claim. Served by inclusionai/ling-2.6-flash. Basic-competence probes: 8/9 passed — only 9 of 12 could be run. Ranking probes: 2/5. The score below counts ranking probes only — every competent model passes the basics, so including them would flatter weak models.

Incomplete run

3 of the 17 probes could not be executed — the provider returned an error rather than an answer, so those checks are missing rather than failed. Ratios above are passes out of probes that actually ran, not out of the full suite.

CapabilityScoreConfidence
coding 100/100 0.333 — too low to rank on
document understanding 0/100 0 — too low to rank on
factuality 100/100 0.333 — too low to rank on
instruction following 100/100 1
long context 100/100 0 — too low to rank on
math 66.67/100 1
multilingual 100/100 0.333 — too low to rank on
reasoning 0/100 0.333 — too low to rank on
safety 100/100 0.333 — too low to rank on

Every probe, pass or fail

Published so the score can be audited rather than trusted. Ranking probes carry the score; basics are a floor check.

ProbeCapabilityTierResult
instruction-exact-word instruction following basic pass
math-rectangle-area math basic pass
math-percentage-chain math ranking pass
reasoning-relative-order reasoning basic fail — expected "chloe", got "ben"
reasoning-negation reasoning basic fail
format-json-extract document understanding basic fail
long-context-needle long context basic fail
instruction-negative-constraint instruction following basic pass
multilingual-exact-translation multilingual basic pass
safety-over-refusal safety basic pass
factuality-stable factuality basic pass
coding-trace-output coding basic pass
math-compound-ordering math ranking fail — expected 30.24 (±0.005), got 31.36
long-context-needle-distractors long context basic pass
reasoning-date-arithmetic reasoning ranking fail — expected "2026-03-04", got "2026-03-03"
instruction-conflicting-order instruction following ranking pass
document-computed-field document understanding ranking fail — JSON mismatch: got {"orderNumber":88,"lineCount":2,"goodsTotal":45.5,"grandTotal":51.5}

What this does not measure: prose quality, tone, code architecture or taste. Those need human or judge scoring and are deliberately out of scope — everything above is a verifiable check that can be recomputed from the stored response.

At 1M requests/month (1500 in / 500 out tokens), moving from Novita to OpenRouter (default route) saves about $270/month.

Illustrative traffic shape, stated so you can check it against your own. Same weights both sides — but confirm quantisation and context below before switching.

Every provider and service tier

Standard on-demand rows first, cheapest of each group first. Tiers are not interchangeable — see the note below the table.

Provider In /1M Out /1M Blended /1M Tier Region Source
OpenRouter (default route) * cheapest standard
resells other hosts
$0.0100 $0.0300 $0.0150 standard provider_api
Novita $0.1000 $0.3000 $0.1500 standard provider_api

* OpenRouter chooses a host for each request, and the figure above is what its default choice costs — not a fixed rate for this model. Selecting a specific host through the same account costs whatever that host charges, which is why this row can sit above or below the hosts listed beside it. Token rates are passed through from the underlying provider with no markup, so the per-1M price above is what that host charges directly — OpenRouter's own charge sits on funding instead: 5.5% (min $0.80) by Stripe or 5% by Coinbase to buy credits, 5% of the equivalent spend if you bring your own provider key. So budget about 5% above the rate shown unless you already hold credit. Fee schedule.

Compare Ling-2.6-flash with alternatives

One model per vendor, ordered by how prominent the vendor is and how many independent providers serve the model — hosts only carry what customers ask for, so that is a real demand signal. This is not traffic or popularity data, which we do not have. Every alternative below does the same job — text generation — because a price comparison between a generator and, say, an upscaler is arithmetic rather than advice. Prices on both sides are standard on-demand rates, so the comparison is like for like — a discounted batch or flex rate is never shown against another model’s standard rate.

AlternativeVendor $/1M vs Ling-2.6-flash QualityKindContextHosts
Qwen3 VL 30B A3B Instruct Alibaba $0.2275
Alibaba
1417% dearer 40/100 standard
open weights
262k 8
Llama 4 Scout Meta $0.1500
OpenRouter (default route)
900% dearer 40/100 standard 1.31M 4
Mistral Small 3.2 24B Mistral $0.1062
OpenRouter (default route)
608% dearer 20/100 standard 256k 3
GPT-4o-mini OpenAI $0.2625
OpenAI
1650% dearer 40/100 standard 128k 3
Nova Lite 1.0 Amazon $0.1050
OpenRouter (default route)
600% dearer 40/100 standard 300k 2
Command R7B (12-2024) Cohere $0.0656
Cohere
337% dearer 0/100 standard 128k 1
Granite 4.0 Micro IBM $0.0408
OpenRouter (default route)
172% dearer 20/100 standard
open weights
131k 1
Gemma 3 12B Google $0.0625
Novita
317% dearer not measured standard
open weights
131k 5

Prices are the cheapest host for each model, blended at a 3:1 input:output ratio. "Quality" is our own probe suite where we have run it — see what it measures. A cheaper alternative is only a real saving if it also passes on the capability your workload needs.

Before you switch

Identical weights do not guarantee identical output. Check that the cheaper host serves the model at full precision rather than a quantised copy, that its context window matches what you need, and that its rate limits carry your peak traffic. A cheaper host that cannot take your throughput is not cheaper — it is an outage.

Move to OpenRouter (default route) without a rewrite

VernaOne fronts every provider on this page with one API, so switching host is a config change — with automatic fallback if quality or latency regresses.

Try VernaOne free →

Frequently asked

How much does Ling-2.6-flash cost?

The cheapest standard-tier provider is OpenRouter (default route) at $0.0150 per 1M tokens blended (3:1 input:output). The dearest is Novita at $0.1500 — a 10.0× difference for the same weights. Prices change often; verify with the provider before budgeting.

Which provider is best for Ling-2.6-flash?

Cheapest is not automatically best. Compare quantisation (a lossy copy of the weights changes output), the context window each host actually serves, and rate limits. Where a provider serves less context than the model's native window we flag it in the table above.

Can I switch providers for Ling-2.6-flash without changing code?

Yes, if you route through an abstraction. VernaOne exposes one API across every provider listed here, so switching host is a config change and you keep automatic fallback if the new one degrades.

← All models · Generated catalog of AI models and every provider that serves them, with normalised prices in USD per 1,000,000 tokens. Blended prices assume a 3:1 input:output ratio.