NoSQL databases for small business buyers
Asked as “NoSQL database”, and as “document or key-value database service”, on behalf of a small B2B company. 52 first choices recorded across the direct, paraphrase, budget and scale prompts, twelve models each.
Standing · first-choice share
77%
MongoDBClear leader
77MongoDB08Google Cloud Firestore04PostgreSQL12others
77% of first choices, clear leader.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment; they sit side by side and are never added together.
The standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all twelve models, for a small B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
Show the five products at 0%, ordered by negative rate
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its product page.
Google Cloud Firestore is made by Google, whose model Gemini 3.5 Flash is in the set. On the four share prompts that model made it the first choice zero times of 4; the other eleven models four times of 44. Lab treatment is defined on the method page; the row is marked, not excluded.
Head to headHead-to-head pages are published for mid-market buyers, so these open on mid-market figures, not small business.
All twenty-one head-to-head pages: the top seven products, each against each
MongoDB vs Azure Cosmos DBMongoDB vs PostgreSQLMongoDB vs Neo4jAmazon DynamoDB vs CouchbaseAmazon DynamoDB vs RedisAmazon DynamoDB vs Azure Cosmos DBAmazon DynamoDB vs PostgreSQLAmazon DynamoDB vs Neo4jCouchbase vs RedisCouchbase vs Azure Cosmos DBCouchbase vs PostgreSQLCouchbase vs Neo4jRedis vs Azure Cosmos DBRedis vs PostgreSQLRedis vs Neo4jAzure Cosmos DB vs PostgreSQLAzure Cosmos DB vs Neo4jPostgreSQL vs Neo4j
One product takes 77% of first choices here, so the chart would put ten markers in one corner and one at the far edge. The two measurements it plots are columns in the standing above: share, and the negative label rate. Five products carry a negative rate above 25% in this category.
What they warned about
Eight of twelve models held their first choice under the paraphrase. Claude Haiku 4.5, Mistral Small, Llama 4 Maverick and Kimi K2 changed. A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Apache Cassandra62%
23 of 37 labels negative · 11 of 12 models · 6 hard negative
“Databases like Apache Cassandra and HBase require significant expertise.... introduce operational complexity that small teams may struggle to manage.” MiniMax M2.5, negative prompt
Apache HBase90%
9 of 10 labels negative · 9 of 12 models · 6 hard negative
“Best avoided unless you already operate Hadoop infrastructure, which most small businesses don't.” DeepSeek V4 Flash, negative prompt
Amazon DynamoDB17%
9 of 54 labels negative · 7 of 12 models
“should be cautious about or avoid include those that are not suitable for simple needs, such as key-value databases like Amazon DynamoDB” Llama 4 Maverick, negative prompt
Neo4j37%
7 of 19 labels negative · 6 of 12 models · 2 hard negative
“Graph Databases (e.g., Neo4j) ... Why to Avoid: Query Complexity ... Cost & Resources” Qwen 3.7 Flash, negative prompt
What they cite
Twelve of the twelve models returned a source list in this edition: four return the pages they cited and eight the search results they consulted.
Sites the answers cite
54 of 72 answers in this category came back with a source list, from 12 of 12 models: citations where the model returns them, or the search results it consulted. 663 links across 215 sites, every framing counted. Ranked by the number of answers carrying the site or page.
vendor site · SourceForge27 answers · 30 citations · 9 models
vendor site · Guideflow25 answers · 25 citations · 9 models
vendor site · Aerospike23 answers · 23 citations · 8 models
vendor site · G221 answers · 34 citations · 9 models
vendor site · MongoDB17 answers · 22 citations · 8 models
14 answers · 14 citations · 6 models
13 answers · 13 citations · 6 models
11 answers · 11 citations · 7 models
10 answers · 15 citations · 7 models
10 answers · 13 citations · 6 models
vendor site · Amazon10 answers · 11 citations · 9 models
10 answers · 11 citations · 7 models
Pages the answers cite
The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.
25 answers · 9 models
21 answers · 8 models
21 answers · 8 models
14 answers · 6 models
13 answers · 6 models
12 answers · 8 models
9 answers · 8 models
9 answers · 6 models
8 answers · 6 models
8 answers · 8 models
Search against answers
Each company's standing in the answers beside its site's footprint in Google search, one row a site: the products the models named on it with their shares, and the share they add up to; monthly searches on Google, and DataForSEO's estimate of AI search demand (modeled from search signals, directional, not a count of queries to any assistant), for the most-searched of the company's and its products' names (the name is in each row's hover text); estimated monthly organic visits to the site; and its best position in Google's top ten for “best nosql database”, “nosql database”, “nosql databases”. US estimates from DataForSEO and Google's Ads Transparency Center. A small company's site, or a mid-sized company's site for its flagship, is marked company; a product on a large parent's site (Google, Microsoft) has no site figures. A column with no figures for this category is left out, and an empty cell means none were seen, not none exist. Two measurements side by side: neither is read as the cause of the other.
| Company and products | Share | Own site cited | Name searches, Google | AI search demand, est. | Organic visits | Paid search a month, est. | Google ads, last 30 days | Best Google position |
|---|---|---|---|---|---|---|---|---|
| MongoDB mongodb.com | 77% | 177 | 74,000 | 9,630 | 342,632 | $8,625 | 120 | 1 |
| Google Cloud google.dev Google Cloud Firestore 8%, Google Cloud Bigtable 0%, Google Firebase / Firestore 0% | 8% | 47 | 165,000 | 7,596 | 881,346 | 109 | ||
| PostgreSQL postgresql.org | 4% | 77 | 74,000 | 14,143 | 465,585 | |||
| Amazon aws.amazon.com Amazon DynamoDB 2%, Amazon RDS 0% | 2% | 164 | 151,000,000 | 1,380,939 | 9,238,321 | $20,476 | 5 | |
| Redis redis.io | 2% | 107 | 27,100 | 6,980 | 112,424 | 18 | ||
| RavenDB ravendb.net | 2% | 9 | 260 | 9 | 1,011 | 7 | ||
| Firebase firebase.google.com Firebase 2%, Firebase Realtime Database 0% | 2% | 6 | 90,500 | 3,437 | 91,247 | |||
| Couchbase couchbase.com | 2% | 85 | 4,400 | 221 | 62,564 | $34 | 26 | |
| restdb.io restdb.io | 2% | |||||||
| Supabase supabase.com | 0% | 68 | 165,000 | 1,974 | 91,424 | 4 |
Google's top ten for the three searches hold 14 sites; 5 of them are among the sites the models cited here (aerospike.com, aws.amazon.com, mongodb.com, pingcap.com, reddit.com). The first result for “best nosql database” is reddit.com.
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.ShowHide
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ShowHideModelDirect“What is the best NoSQL database for a small B2B company?”Paraphrase“Which document or key-value database service would you recommend to a small business that sells to other businesses?”Comparative“What are the top NoSQL databases for a small team and how do they differ?”Budget-constrained“What is the best NoSQL database for a small company with a tight budget?”Scale-constrained“We are a 30 person company evaluating a NoSQL database. What should we look at?”Negative“Which NoSQL databases should a small business avoid or be cautious about?”
Claude Haiku 4.5MongoDB, PostgreSQLagainst: Amazon DynamoDBGoogle Cloud Firestore, MongoDBChangedFirebase Realtime Database, Google Cloud Firestore, MongoDBFirebase, MongoDBagainst: Apache Cassandrano first choicenothing named
One alternative
Google Cloud FirestoreTwo alternatives
Amazon DynamoDB, RedisOne alternative
RedisOne alternative
Amazon DynamoDBGPT-5.4 miniMongoDBMongoDBHeldGoogle Cloud Firestore, MongoDBagainst: Apache Cassandra, CouchbaseMongoDBagainst: Google Cloud Firestore, Redisno first choiceagainst: Apache Cassandra, Apache HBase, Couchbase, MongoDB, Neo4j, Redis
Two alternatives
Amazon DynamoDB, CouchbaseTwo alternatives
Amazon DynamoDB, Google Cloud FirestoreTwo alternatives
Amazon DynamoDB, RedisOne alternative
PostgreSQLOne alternative
PostgreSQLGemini 3.5 FlashMongoDBMongoDBHeldMongoDBMongoDBPostgreSQLagainst: Apache Cassandra, Neo4j, ScyllaDBagainst: Amazon DynamoDB, Apache Accumulo, Apache Cassandra, Apache CouchDB, Apache HBase, Google Cloud Firestore, Neo4j, Riak KV, ScyllaDB, TigerGraph
Three alternatives
Amazon DynamoDB, Google Cloud Firestore, PostgreSQLTwo alternatives
Amazon DynamoDB, Google Cloud FirestoreThree alternatives
Amazon DynamoDB, Google Cloud Firestore, RedisThree alternatives
Amazon DynamoDB, Google Cloud Firestore, PostgreSQLThree alternatives
Amazon DynamoDB, Google Cloud Firestore, MongoDBPerplexity SonarMongoDBMongoDBHeldMongoDBagainst: Apache CassandraGoogle Cloud Firestore, MongoDBagainst: Apache Cassandra, ScyllaDBno first choiceagainst: Apache Cassandra, Apache HBase, MongoDB, Neo4j, Redis, ScyllaDB
Three alternatives
Amazon DynamoDB, RavenDB, RedisTwo alternatives
Amazon DynamoDB, Google Cloud FirestoreSix alternatives
Amazon DynamoDB, Azure Cosmos DB, Couchbase, Neo4j, RavenDB, RedisGrok 4.20MongoDBMongoDBHeldagainst: RedisMongoDBagainst: Apache Cassandra, CouchbaseMongoDBMongoDBagainst: Apache Cassandraagainst: Apache Cassandra, Apache HBase, Couchbase, MongoDB, Oracle NoSQL Database, Redis, Self-Hosted Elasticsearch
Four alternatives
Amazon DynamoDB, Apache Cassandra, Google Cloud Firestore, RedisThree alternatives
Amazon DynamoDB, Google Cloud Firestore, RavenDBTwo alternatives
Amazon DynamoDB, RedisFour alternatives
Apache Cassandra, Apache CouchDB, PostgreSQL, RedisFour alternatives
Amazon DynamoDB, Google Cloud Bigtable, Redis, ScyllaDBMistral SmallMongoDBGoogle Cloud Firestore, MongoDBChangedApache CouchDB, MongoDBGoogle Cloud Firestore, MongoDBagainst: Apache CassandraCouchbase, MongoDBagainst: Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, ScyllaDBagainst: Apache Cassandra, Apache HBase, Neo4j
Two alternatives
Firebase, restdb.ioTwo alternatives
Amazon DynamoDB, ValkeyThree alternatives
Apache Cassandra, RavenDB, RedisOne alternative
RavenDBThree alternatives
ArangoDB, Neo4j, RedisDeepSeek V4 FlashMongoDBagainst: Amazon DynamoDBMongoDBHeldMongoDBagainst: Apache CassandraMongoDB, Redisagainst: Apache CassandraMongoDBagainst: Apache Cassandra, ScyllaDBagainst: Amazon DynamoDB, Apache Cassandra, Apache CouchDB, Apache HBase, Couchbase, MongoDB
One alternative
PostgreSQLTwo alternatives
Amazon DynamoDB, Google Cloud FirestoreThree alternatives
Amazon DynamoDB, Google Cloud Firestore, RedisOne alternative
Amazon DynamoDBTwo alternatives
Amazon DynamoDB, RedisLlama 4 Maverickno first choiceMongoDBChangedno first choicerestdb.iono first choiceagainst: Amazon DynamoDB, Couchbase, Oracle NoSQL Database
Three alternatives
Amazon DynamoDB, Azure Cosmos DB, RedisTwo alternatives
Apache Cassandra, Azure Cosmos DBQwen 3.7 FlashMongoDBMongoDBHeldMongoDBMongoDBno first choiceagainst: Apache Cassandra, RethinkDB, Riak KV, ScyllaDBagainst: Apache Cassandra, Apache HBase, Memcached, MongooseIM, Neo4j, WorldServer
Three alternatives
Amazon DynamoDB, Google Cloud Firestore, Neo4jThree alternatives
Amazon DynamoDB, Firebase, SupabaseTwo alternatives
Google Firebase / Firestore, RedisThree alternatives
Amazon DynamoDB, Firebase, RedisTwo alternatives
MongoDB, PostgreSQLKimi K2RavenDBMongoDBChangedagainst: Amazon DynamoDBMongoDBMongoDBMongoDBagainst: Amazon DynamoDB, Apache Cassandra, MongoDB
Two alternatives
MongoDB, PostgreSQLOne alternative
SupabaseFour alternatives
Couchbase, Google Cloud Firestore, PostgreSQL, RedisThree alternatives
Apache CouchDB, PostgreSQL, RavenDBThree alternatives
Amazon DynamoDB, Couchbase, RedisGLM 4.7 FlashXMongoDBMongoDBHeldMongoDB, RedisMongoDBagainst: MongoDBno first choiceagainst: Amazon DynamoDB, Amazon Neptune, Apache Cassandra, Apache CouchDB, Apache HBase, ArangoDB, Couchbase, MongoDB, Neo4j, Oracle NoSQL Database, Redis
Four alternatives
Amazon DynamoDB, Azure Cosmos DB, Google Cloud Firestore, RedisTwo alternatives
Amazon DynamoDB, RedisFive alternatives
Amazon DynamoDB, Apache Cassandra, Couchbase, InfluxDB, Neo4jFive alternatives
Apache Cassandra, Apache CouchDB, PostgreSQL, Redis, SQLiteMiniMax M2.5MongoDBMongoDBHeldMongoDBMongoDBAmazon DynamoDB, MongoDBagainst: Apache Cassandra, Apache HBase
Two alternatives
Firebase, RavenDBTwo alternatives
Amazon DynamoDB, Google Cloud FirestoreFour alternatives
Amazon DynamoDB, Apache Cassandra, Google Cloud Firestore, RedisThree alternatives
Amazon DynamoDB, Apache CouchDB, RedisTwo alternatives
PostgreSQL, RedisBold is the first choiceAlternatives are counted; the count opens them.What the answer argued against
The record
One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency.
Seventy-two rows: every prompt, every model, every answer.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|---|---|---|---|---|---|
| Direct recommendation | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 09:57 | no | 0 | 5 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 13:07 | yes | 5 | 5 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 11:23 | yes | 6 | 21 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-09-17 10:07 | yes | 20 | 5 s |
| Direct recommendation | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 12:39 | yes | 18 | 10 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 11:06 | yes | 5 | 4 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 10:18 | yes | 10 | 15 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 11:41 | yes | 5 | 3 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 12:13 | no | 0 | 41 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:59 | yes | 15 | 49 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 12:24 | yes | 15 | 92 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 12:41 | yes | 6 | 91 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 11:47 | no | 0 | 5 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 09:43 | yes | 3 | 5 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 13:15 | yes | 8 | 21 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-09-17 11:34 | yes | 17 | 5 s |
| Paraphrase | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 14:09 | yes | 14 | 8 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 13:12 | yes | 10 | 7 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 12:09 | yes | 15 | 18 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 11:57 | yes | 5 | 3 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 13:45 | no | 0 | 34 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:28 | yes | 20 | 24 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 10:48 | no | 0 | 14 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 13:33 | yes | 5 | 37 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 10:12 | no | 0 | 5 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 11:24 | no | 0 | 6 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 12:46 | yes | 10 | 26 s |
| Comparative | Perplexity Sonar | sonar | 2026-09-17 13:56 | yes | 20 | 8 s |
| Comparative | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 10:01 | yes | 11 | 9 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 09:38 | yes | 10 | 10 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 13:06 | yes | 10 | 17 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 09:52 | yes | 5 | 3 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 10:19 | no | 0 | 41 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 09:50 | yes | 10 | 41 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 13:26 | yes | 10 | 109 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 12:35 | yes | 5 | 64 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 13:12 | no | 0 | 4 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 13:12 | no | 0 | 3 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 12:44 | yes | 7 | 24 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-09-17 12:57 | yes | 20 | 5 s |
| Budget constrained | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 09:56 | yes | 15 | 8 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 12:03 | yes | 5 | 5 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 10:26 | yes | 10 | 13 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 13:18 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 10:32 | no | 0 | 35 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 12:43 | yes | 15 | 46 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 10:44 | yes | 17 | 85 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 13:16 | yes | 5 | 52 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 11:34 | no | 0 | 5 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 11:01 | no | 0 | 9 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 14:14 | no | 0 | 15 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-09-17 14:10 | yes | 20 | 14 s |
| Scale constrained | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 11:32 | no | 0 | 6 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 14:01 | no | 0 | 6 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 10:33 | yes | 14 | 22 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 12:00 | yes | 5 | 4 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 11:02 | no | 0 | 36 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 13:35 | yes | 20 | 33 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 09:35 | yes | 10 | 24 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 12:43 | yes | 5 | 38 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 13:12 | yes | 17 | 8 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 14:12 | no | 0 | 6 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 13:18 | yes | 9 | 27 s |
| Negative framing | Perplexity Sonar | sonar | 2026-09-17 09:53 | yes | 20 | 8 s |
| Negative framing | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 11:12 | yes | 39 | 20 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 12:45 | yes | 5 | 7 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 11:28 | yes | 30 | 25 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 12:55 | yes | 5 | 3 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 09:41 | yes | 15 | 48 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:54 | yes | 20 | 34 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 13:42 | yes | 28 | 118 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 09:23 | yes | 9 | 41 s |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
ShowHide
Category-scoped readings
Firebase / Cloud Firestore read as Google Cloud Firestore
Google Cloud Firestore / Firebase read as Google Cloud Firestore
MongoDB Atlas read as MongoDB
Oracle NoSQL read as Oracle NoSQL Database
Riak read as Riak KV
Unresolved, counted raw
Apache Accumulo
Convex
MongooseIM
TigerGraph
WorldServer
Discontinued, still offered
No shut-down product was recommended here.