Applied artificial intelligence, neural systems, and smart agents.

Fusing generative AI, multi-agent frameworks, and high-throughput data processing to automate business workflows.

an engineered reference, not a listicle

For each workload, which model meets spec

We compute each category's ranking from stated specifications and publish the calculation on the same page. A figure with no supporting document does not enter the output, and every page carries its own last-inspection date.

  • ranking computed, not opinion-driven
  • every figure carries a source record and a timestamp
  • an Iran-access status column

How a ranking in this section is calculated

Three inputs go into one calculation and a table comes out. All three are visible on the page itself, and the fourth input is deliberately left unconnected.

  • 1The criteria

    Each category has its own criteria, and they are written on that same page.

  • 2The weights

    Every weight is visible. Change a weight and the table recomputes.

  • 3A source per number

    From the maker, or from the benchmark own leaderboard. Unverified stays unverified.

  • A cell with no source

    Scoring zero and being unverified are two different things, and they stay two different things in the table. This input never enters the calculation and the cell stays empty.

  • The ranking calculation

    Change a weight and the table recomputes. There is no manual position field in this section at all.

  • The ranking table

    The arithmetic sits beside the table, plus an access from Iran column with the date it was checked.

The rankings here are temporary, and that is correct: any new release can move the table.

Which workload are you assigning to AI

Each workload carries a ranking table whose criteria and weights are documented on that same page.

Chat and assistants Each model rank, extracted from a leaderboard that logs a measurement date and a vote count beside every number. Current pick Claude Fable 5 the maker has replaced it with Claude Fable 5.1 Studying and learning The tool that converts your raw source material into several reviewable formats, not a single answer meant to be read once. Current pick Gemini Notebook Video generation The component that delivers greater capacity, cleaner output and more reference inputs in a single generation pass. Current pick Seedance 2.5 Coding The model with the highest operating throughput for coding, based on the web-dev leaderboard and real pricing. Current pick Claude Opus 5 Image generation The model with the highest output image quality, against the cost you actually pay. Current pick GPT Image 2 Music generation Which engine produces higher audio quality, and which one actually lets you keep the file at the end of the pipeline. Current pick Suno Voice Which model text-to-Persian-speech pipeline actually works, and which one only claims multilingual coverage. Current pick ElevenLabs v3 Translation Which translation model declares Persian in its technical specification, and which one is actually deployable. Current pick Command A Translate

If you already know the specification you need and just want the numbers: Full model table

Why this page's architecture differs from other lists

The ranking calculation runs in the open, on the page

Each category has its own specifications, each specification carries a weight, and every weight is visible. Change one weight and the table recomputes. There is no field called "manual position" anywhere in this module, so no ranking shifts without a traceable change.

We log missing data as missing data

If a figure cannot be verified against the vendor's source or the benchmark's own leaderboard, the cell stays empty and is tagged "unverified". Scoring zero and being unverified are two distinct failure modes, and the table keeps them as two distinct values.

A column that only direct measurement can fill

For every tool we record whether it is reachable from Iran, whether card payment functions, and where it does not, what alternative actually works. We run this check ourselves from an Iranian connection and log the date it was run.

One page, two independent language builds

Every page ships a Persian build and an English build, and each is authored separately rather than translated. The facts and the figures match across both; the sentences do not.

The vendor index

Each vendor has three layers: the products you sign in to, the model families, and the versions.

Anthropic Builder of Claude. Currently the most stable pick for coding workloads and long-running agents. Google The widest infrastructure on the market. The only maker shipping operational units in all six categories. Moonshot AI The vendor behind Kimi. The lowest-cost path to a model holding third rank in our coding table. Z.ai The vendor behind GLM. The only vendor in this group logging a zero-dollar row on its own pricing table. DeepSeek The lowest-cost unit in our coding performance table, backed by the most thoroughly documented release history in this group. Alibaba Builder of the Qwen line. The one catalogue in this group that has not registered the specifications of the model the leaderboard ranks. OpenAI The vendor behind ChatGPT. Its published technical documentation omits some specifications. xAI The vendor behind Grok. The only vendor in this reference documenting the price of a tool invocation. ByteDance Builder of Seedance. Its model holds the top rank in the video table, and its documentation has fallen behind actual deployment. Kuaishou The Chinese company behind the Kling infrastructure. The maker name is rarely searched; its product is the most searched of all. Runway The maker of Gen-4.5, and the distribution layer that also serves its rivals. Mistral AI The French company shipping its flagship model with open weights under the Apache 2.0 licence. Cohere The Canadian vendor that registers Persian by name in its specifications, and has blocked all three routes to it. Amazon Both the builder of Nova and the distribution layer for other vendors through Bedrock. Do not treat these two layers as one. Meta The maker of Llama. Weights are published, but the licence differs from common assumption. ElevenLabs The best known AI voice infrastructure. Only one of its units supports Persian processing.

Latest logged changes

  • The Claude Fable 5.1 record was logged; deployment date 1 September 2026. Audit result: the base price parameter is unchanged, $10 in and $50 out, matching Fable 5 exactly. The only parameter modified in Anthropic pricing table is the cache read, from one dollar to twenty-five cents. Anthropic flags this in a footnote under that same table as an exception in the pricing structure, because the cache multiplier is fixed at 0.1x input price across the entire Claude line and drops to 0.025x only for Fable 5.1 and Mythos 5.1. The benchmark jump is not uniform either: agentic scientific research moves from 24.7 to 52.6, terminal coding from 42.0 to 55.8, CursorBench only from 70.5 to 73.4. The model was not entered into any ranking table on this site; this is an operational decision: the coding rubric allocates 70 of 100 weight units to two Arena leaderboards, and this one-day-old model has not yet been scored on either, so entering it into the category would have produced only an unranked row under the table. Mythos 5.1 was added to the catalogue and Fable 5 was moved to legacy status. Claude Fable 5.1
  • The translation category was deployed, the first of tranche two. Audit result: two parameters have come apart here. The model that registers your language by name in its own list is not procurable, and the model that is downloadable and runnable does not name your language. Command A Translate is the only model whose maker both classifies it as a translation model and lists all four of this site languages in one 23-language list; Llama 4, which carries a commercial licence, ships a closed twelve-language list with neither Persian nor Turkish on it. Licence-openness weight in this category is 30 of 100, a figure not repeated in any other category, because in Iran exactly that hosted API is the link that is down. One criterion was tested and then removed: context window, because min-max normalisation with a floor of 8,000 and a ceiling of ten million reported a thirty-two fold difference as 0.4 against 0.2. Translation
  • The Cohere record was logged; a maker that until this deployment held only a few rows in the catalogue. Audit result: Cohere is the only maker in this reference that registers Persian item by item in a model language list, in Command A Translate and in Aya Expanse 32B, and all three access routes to that Persian are blocked separately. The translation model carries no published price, the open-weight model holds a CC BY-NC licence meaning commercial use is prohibited, and the model licensed under Apache 2.0 with commercial use permitted does not cover Persian at all. Separately, Cohere own commercial agreement registers Iran by name in its Restricted Location definition, differing from every other maker practice here: the others omit Iran from a list, Cohere enters it explicitly. Cohere
  • The Amazon record was logged. Two findings from this audit: first, the Nova specification table prints "+200" in the Supported Languages cell, and that cell footnote lists fifteen languages; Arabic and Turkish are among the fifteen and Persian is not. Same pattern as ElevenLabs, this time surfacing at footnote level. Second, Nova access capacity is a data-center list rather than a country list, and neither list registers a Middle East region; the nearest deployment point for a Nova model is Mumbai or Frankfurt. Nova pricing was also not recorded, because the Bedrock pricing page renders its figures in JavaScript. Amazon

Change log

A direct note on this page's operating limits

We built this reference because our own clients keep asking which tool to select for a given workload, and the answer shifts every quarter. So the rankings here are provisional by design: any new release can move the table.

What you will not find on this page is an account for sale. We sell no subscriptions and no placement in any table. If work needs doing with these tools, the corresponding service is documented separately on the site.

What the AI reference gives you for picking an AI tool

This is not a list of tastes. For each job we work out which model meets the spec needed and put the calculation on the same page. Each page also shows its last-checked date.

8workloadsChat, studying, video, coding, image, music, voice, translation
3inputsCriteria, weights and source
12Aug 2026The last-checked date

Tap any step to read the details behind it.

Innovative solutions for picking an AI tool

The ranking method is open on the page, and you can see every weight.

Tap any solution to see exactly what it includes.

Six principles for ranking AI tools

We set Arena, Artificial Analysis and the NIST framework beside the method of Seemerce's AI reference.

1

Criteria per workload

Artificial Analysis measures quality, speed, price and latency separately; Arena ranks models in categories such as text, WebDev and vision.

With us Each category has its own criteria, written on the same page.

2

Visible weights

NIST AI RMF names Measure as one of four risk-management functions: Govern, Map, Measure and Manage.

With us Every weight is visible; change one and the table recomputes. The weights are our only judgment call.

3

A source for every number

Artificial Analysis says it measures end-to-end performance as customers experience it, not theoretical maximums.

With us Each number comes from the maker or the benchmark's own leaderboard; unverified stays unverified.

4

Zero is not unknown

Arena counts 356,692,404 sessions on its leaderboard.

With us An empty cell means we could not verify that number, not that the tool scored zero.

5

The date checked

NIST says the framework is voluntary and under revision.

With us Every page carries a last-checked date; any new release can move the table and rankings are temporary.

6

Access from Iran

These references have no access-from-Iran column.

With us An Access from Iran column sits beside the table, with the date checked and how it was extracted.

Sources: Arena (arena.ai); Artificial Analysis; NIST AI RMF

Six numbers

Each figure is from its own source.

8workloads

Ranked here

Chat, studying, video, coding, image, music, voice and translation.

Seemerce
356Msessions

Arena leaderboard

Sessions counted on Arena's leaderboard.

Arena
4functions

NIST AI RMF

Govern, Map, Measure and Manage.

NIST
2023

AI RMF 1.0

The year of version 1.0 (26 January).

NIST
12Aug 2026

Last checked

The last-checked date of the ranking pages.

Seemerce
0toman

Free

Free with no sign-up.

Seemerce

Sources: Arena (arena.ai); Artificial Analysis; NIST AI RMF

Two ranking references and Seemerce

Only what each platform's page says outright is marked; a blank means the page does not say.

PracticeArenaArtificial AnalysisSeemerce
Ranking from human votes1 of 2Arena's leaderboard rank goes into the calculation
Price per token1 of 2A score-per-dollar measure
Speed and latency1 of 2Not measured on this page
Weights are visible and changeable0 of 2Every weight is on the page; change one and the table recomputes
An access-from-Iran column0 of 2With the date it was checked

stated on their published page   not stated there (it may still be done)

Sources: Arena (arena.ai); Artificial Analysis; NIST AI RMF

How many tools does each table cover?

The number of models or tools in the three published tables.

9 models4 tools4 tools
Chat and assistantsStudying and learningVideo generation
The tables are small and hold only what can be documented.

Sources: the Seemerce AI reference pages (12 August 2026)

What this page offers

Only what this page already promises.

Method

  • Criteria, weights and sources on the same page
  • A table that recomputes when a weight changes
  • No manual position field

Honesty

  • An empty cell means unverified
  • Rankings are temporary
  • A last-checked date

Iran

  • An access-from-Iran column
  • The date and the method
  • No unsupported claim

How often people search for this, in four markets

AI image generation runs 823,000 searches a month and the click costs $0.85. Enormous attention, almost no commercial value per visitor. That single ratio explains why so many AI tools struggle to convert traffic into revenue.

Term enteredMonthly search volumeClick costRanking difficultyUser goal
ai image generator823,000$0.8593Informational
ai video generator165,000$1.7191Commercial
ai chatbot90,500$0.6895Commercial
ai tools27,100$3.8991Informational + Commercial
best ai tools6,600$4.5773Commercial

Data from Semrush, US database, retrieved 27 August 2026. Search volumes change over time, so treat anything older than three months as a trend rather than an exact figure.

See+Mind=SeeMind This page's name in the Seemerce family What the name means
This page's name in the Seemerce family

AI models and tools, scored against a published rubric.

The rule is simple: See joins a word to make one new word, just as See and commerce became Seemerce.

Related names