NoSQL databases for enterprise buyers
Asked as “NoSQL database”, and as “document or key-value database service”, on behalf of an enterprise B2B company. 45 first choices recorded across the direct, paraphrase, budget and scale prompts, twelve models each.
Standing · first-choice share
53%
Clear leader
53MongoDB24Amazon DynamoDB04Azure Cosmos DB18others
53% 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 an enterprise B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
Show the three 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.
Head to headHead-to-head pages are published for mid-market buyers, so these open on mid-market figures, not enterprise.
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
Recommended versus criticized
Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
Criticized challengerCriticized default
Negative label rate →
01
02
03
04
05
06
07
08
09Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative70%
Key
01MongoDB53%
MongoDB53%02Amazon DynamoDB24%
Amazon DynamoDB24%03Azure Cosmos DB4%
Azure Cosmos DB4%04Couchbase2%
Couchbase2%05ScyllaDB2%
ScyllaDB2%06Redis2%
Redis2%07Aerospike0%
Aerospike0%08Apache Cassandra0%
Apache Cassandra0%09Neo4j0%
Neo4j0%What they warned about
Five of twelve models held their first choice under the paraphrase. Claude Haiku 4.5, GPT-5.4 mini, Grok 4.20, Mistral Small, Llama 4 Maverick, Kimi K2 and MiniMax M2.5 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 Cassandra29%
10 of 35 labels negative · 9 of 12 models
“Self-managed options like open-source Cassandra or Redis require you to handle SOC 2 controls, which scales poorly at your size.” Grok 4.20, scale prompt
MongoDB19%
12 of 62 labels negative · 8 of 12 models · 1 hard negative
“Databases to Avoid or Be Highly Cautious With: 1. MongoDB (especially self-managed or older versions)” Grok 4.20, negative prompt
Apache CouchDB100%
6 of 6 labels negative · 6 of 12 models · 3 hard negative
“The same enterprise article filters CouchDB out because it does not meet the author's horizontal sharding/partitioning criterion” Perplexity Sonar, negative prompt
Redis23%
9 of 39 labels negative · 6 of 12 models · 2 hard negative
“the main databases to avoid or scrutinize closely are Redis... explicitly filters it out because it is memory-based” Perplexity Sonar, 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
59 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. 873 links across 273 sites, every framing counted. Ranked by the number of answers carrying the site or page.
vendor site · Aerospike35 answers · 47 citations · 11 models
vendor site · MongoDB24 answers · 45 citations · 10 models
vendor site · Couchbase22 answers · 43 citations · 9 models
21 answers · 26 citations · 8 models
vendor site · ScyllaDB19 answers · 32 citations · 8 models
vendor site · SourceForge17 answers · 20 citations · 8 models
15 answers · 18 citations · 7 models
15 answers · 15 citations · 8 models
15 answers · 15 citations · 11 models
14 answers · 14 citations · 8 models
13 answers · 20 citations · 9 models
13 answers · 13 citations · 8 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.
34 answers · 11 models
14 answers · 8 models
13 answers · 8 models
12 answers · 9 models
12 answers · 9 models
12 answers · 6 models
12 answers · 6 models
11 answers · 6 models
11 answers · 5 models
10 answers · 5 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 | 53% | 177 | 74,000 | 9,630 | 342,632 | $8,625 | 120 | 1 |
| Amazon aws.amazon.com Amazon DynamoDB 24% | 24% | 154 | 151,000,000 | 1,380,939 | 9,238,321 | $20,476 | 5 | |
| Azure parent site Azure Cosmos DB 4% | 4% | 76 | 301,000 | 47,904 | ||||
| Oracle parent site Oracle NoSQL Database 4% | 4% | 14 | 450,000 | 66,611 | ||||
| Google Cloud google.dev Google Cloud Bigtable 2%, Google Cloud Firestore 2% | 4% | 47 | 165,000 | 7,596 | 881,346 | 109 | ||
| Couchbase couchbase.com | 2% | 85 | 4,400 | 221 | 62,564 | $34 | 26 | |
| ScyllaDB scylladb.com | 2% | 43 | 1,900 | 76 | 22,468 | 120 | 3 | |
| Redis redis.io | 2% | 107 | 27,100 | 6,980 | 112,424 | 18 | ||
| Amazon parent site Amazon DocumentDB 2%, Amazon ElastiCache 0%, Amazon Keyspaces 0% | 2% | 20 | 151,000,000 | 1,380,939 | ||||
| Aerospike aerospike.com | 0% | 22 | 2,900 | 250 | 70,270 | $25 | 24 | 2 |
Google's top ten for the three searches hold 14 sites; 4 of them are among the sites the models cited here (aerospike.com, mongodb.com, pingcap.com, scylladb.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 an enterprise B2B company?”Paraphrase“Which document or key-value database service would you recommend to a large B2B company with thousands of employees?”Comparative“What are the top enterprise-grade NoSQL databases and how do they differ?”Budget-constrained“What is the best NoSQL database for a large company that needs predictable total cost across thousands of users?”Scale-constrained“We are a 5,000 person company with SSO, SOC 2 and procurement review requirements evaluating a NoSQL database. What should we look at?”Negative“Which NoSQL databases should a large enterprise avoid or be cautious about?”
Claude Haiku 4.5MongoDBAmazon DocumentDB, Amazon DynamoDB, Google Cloud FirestoreChangedno first choiceAmazon DynamoDBno first choicenothing named
Four alternatives
Amazon DynamoDB, Apache Cassandra, Elasticsearch, RedisOne alternative
CouchbaseThree alternatives
Apache Cassandra, Azure Cosmos DB, Oracle NoSQL DatabaseGPT-5.4 miniAmazon DynamoDBMongoDBChangedMongoDBAmazon DynamoDBagainst: MongoDBno first choiceagainst: Apache Cassandra, MongoDB
Two alternatives
Apache Cassandra, MongoDBOne alternative
Amazon DynamoDBFive alternatives
Amazon DynamoDB, Apache Cassandra, Couchbase, Oracle NoSQL Database, RedisTwo alternatives
Couchbase, MongoDBGemini 3.5 FlashMongoDBMongoDBHeldMongoDBScyllaDBMongoDBagainst: Apache Cassandra, Apache HBase, MongoDB, OrientDB, Redis, RethinkDB, Riak KV
Five alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase, Neo4jFour alternatives
Amazon DynamoDB, Azure Cosmos DB, Redis, ValkeySix alternatives
Amazon DynamoDB, Apache Cassandra / DataStax Enterprise, Azure Cosmos DB, Couchbase, Neo4j, RedisFour alternatives
Aerospike, Amazon DynamoDB, Azure Cosmos DB, CouchbaseFive alternatives
Amazon DynamoDB, Azure Cosmos DB, Couchbase, Google Cloud Bigtable, RedisPerplexity SonarMongoDBagainst: RedisMongoDBHeldno first choiceGoogle Cloud Bigtableagainst: Amazon DynamoDB, Azure Cosmos DBno first choiceagainst: Apache CouchDB, Neo4j, Redis, Tokyo Cabinet, Voldemort
Three alternatives
Apache Cassandra, Couchbase, ScyllaDBThree alternatives
Amazon DynamoDB, Azure Cosmos DB, CouchbaseOne alternative
AerospikeGrok 4.20MongoDBAmazon DynamoDBChangedno first choiceagainst: Azure Cosmos DBAmazon DynamoDBagainst: MongoDBAmazon DynamoDB, Couchbase, MongoDBagainst: Apache Cassandra, Redisagainst: Apache CouchDB, Couchbase, MongoDB, Neo4j, Redis, Riak KV, Voldemort
Two alternatives
Amazon DynamoDB, Apache CassandraFive alternatives
Amazon ElastiCache, Azure Cosmos DB, Couchbase, MongoDB, RedisThree alternatives
Aerospike, Oracle NoSQL Database, ScyllaDBTwo alternatives
DataStax Astra DB, ScyllaDBMistral SmallAzure Cosmos DB, MongoDBMongoDBChangedCouchbase, MongoDBOracle NoSQL Databaseno first choiceagainst: Apache Cassandra, MongoDB
Two alternatives
Couchbase, RedisThree alternatives
Amazon DocumentDB, Amazon DynamoDB, Azure Cosmos DBFive alternatives
Aerospike, Amazon DynamoDB, Apache Cassandra / DataStax Astra DB, Azure Cosmos DB, RedisTwo alternatives
Amazon DynamoDB, Azure Cosmos DBDeepSeek V4 FlashMongoDBMongoDBHeldagainst: AerospikeCouchbase, MongoDBMongoDBagainst: Apache Cassandra, Azure Cosmos DBno first choiceagainst: Apache Cassandra, Apache CouchDB, Couchbase, RethinkDB, Riak KV, ScyllaDB
Five alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Redis, ScyllaDBFour alternatives
Amazon DynamoDB, Azure Cosmos DB, Couchbase, RedisSix alternatives
Amazon DynamoDB, Apache Cassandra / DataStax Astra DB, Azure Cosmos DB, Neo4j, Redis, ScyllaDBTwo alternatives
Amazon DynamoDB, Oracle NoSQL DatabaseLlama 4 Maverickno first choiceAmazon DynamoDB, MongoDBChangedno first choiceno first choiceno first choicenothing named
Qwen 3.7 FlashMongoDBMongoDBHeldMongoDBAmazon DynamoDBagainst: Apache Cassandra, MongoDBno first choiceagainst: Amazon DynamoDB, Apache Cassandra / DataStax Enterprise, Apache HBase, Avro / Parquet, Elasticsearch, Flare, Google Cloud Firestore, Google Cloud Spanner, InfluxDB, MongoDB, Redis, VAST
Four alternatives
Amazon DynamoDB, Azure Cosmos DB, DataStax Astra DB, Neo4jThree alternatives
Aerospike, Amazon DynamoDB, Azure Cosmos DBSix alternatives
Amazon DynamoDB, Apache Cassandra, Couchbase, Neo4j, Redis, ScyllaDBOne alternative
Azure Cosmos DBKimi K2MongoDBMongoDB, RedisChangedagainst: Amazon ElastiCacheMongoDBagainst: Amazon DynamoDB, Amazon NeptuneMongoDBagainst: Amazon DynamoDB, Azure Cosmos DBMongoDBagainst: Redisagainst: Amazon DynamoDB, Apache Cassandra, Apache CouchDB, Apache HBase, Azure Cosmos DB, Google Cloud Firestore, MongoDB, Redis
Two alternatives
Apache Cassandra, CouchbaseTwo alternatives
Amazon DynamoDB, Azure Cosmos DBFive alternatives
Apache Cassandra, ArangoDB, Couchbase, Neo4j, RedisTwo alternatives
Aerospike, CouchbaseFive alternatives
Amazon DynamoDB, Azure Cosmos DB, Couchbase, DataStax, ScyllaDBGLM 4.7 FlashXMongoDBMongoDBHeldno first choiceOracle NoSQL Databaseagainst: Amazon DynamoDBno first choiceagainst: ActorDB, AnnaDB, Apache Cassandra, Apache CouchDB, Apache HBase, HanoiDB, MongoDB, Neo4j, Redis, Riak KV
Four alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, CouchbaseTwo alternatives
Amazon DynamoDB, Azure Cosmos DBTwo alternatives
Aerospike, DataStax Astra DBMiniMax M2.5Azure Cosmos DB, MongoDBAmazon DynamoDBChangedno first choiceAmazon DynamoDBagainst: Couchbase, MongoDBno first choiceagainst: Amazon DynamoDB, Apache Cassandra, Apache CouchDB, MongoDB
Three alternatives
Amazon DynamoDB, Couchbase, ScyllaDBThree alternatives
Amazon DocumentDB, Azure Cosmos DB, MongoDBOne alternative
Azure Cosmos DBFive alternatives
Amazon DynamoDB, Couchbase, MongoDB, Redis, ScyllaDBBold 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 13:26 | no | 0 | 5 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 13:51 | yes | 3 | 5 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 09:55 | no | 0 | 14 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-09-17 13:19 | yes | 20 | 6 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 10:42 | yes | 33 | 21 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 10:39 | yes | 10 | 8 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 09:46 | yes | 10 | 17 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 11:51 | yes | 5 | 3 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 11:02 | yes | 5 | 25 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:23 | yes | 10 | 36 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 13:33 | yes | 30 | 153 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 11:24 | yes | 9 | 50 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 13:43 | no | 0 | 4 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 13:53 | no | 0 | 5 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 09:47 | yes | 6 | 22 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-09-17 12:31 | yes | 20 | 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 10:43 | yes | 15 | 10 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 12:27 | no | 0 | 4 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 13:20 | yes | 20 | 22 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 10:43 | yes | 5 | 3 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 12:04 | no | 0 | 38 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 13:56 | yes | 20 | 24 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 11:47 | no | 0 | 39 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 10:33 | yes | 5 | 39 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 12:13 | yes | 9 | 9 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 13:59 | yes | 1 | 10 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 10:20 | yes | 10 | 24 s |
| Comparative | Perplexity Sonar | sonar | 2026-09-17 12:36 | yes | 20 | 13 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 13:12 | yes | 14 | 11 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 11:59 | yes | 9 | 10 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 10:32 | yes | 19 | 31 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 12:08 | yes | 5 | 3 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 11:45 | no | 0 | 35 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:50 | yes | 19 | 49 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 13:04 | yes | 24 | 42 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 09:29 | yes | 10 | 47 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 13:11 | yes | 16 | 9 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 11:43 | yes | 3 | 5 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 11:59 | yes | 20 | 26 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-09-17 13:57 | 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 11:41 | yes | 38 | 17 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 12:07 | yes | 5 | 3 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 11:04 | yes | 16 | 23 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 10:57 | yes | 5 | 4 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 09:28 | yes | 9 | 73 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:17 | yes | 19 | 41 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 11:52 | yes | 27 | 109 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 12:31 | yes | 22 | 146 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 12:16 | no | 0 | 7 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 13:14 | yes | 2 | 8 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 09:36 | no | 0 | 17 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-09-17 12:37 | yes | 20 | 10 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 12:21 | yes | 17 | 12 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 09:42 | no | 0 | 7 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 11:11 | yes | 20 | 22 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 11:51 | yes | 5 | 4 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 12:59 | no | 0 | 38 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:43 | yes | 10 | 62 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 12:54 | yes | 29 | 101 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 09:55 | yes | 8 | 50 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 09:24 | yes | 18 | 10 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 09:35 | yes | 4 | 8 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 09:45 | yes | 19 | 28 s |
| Negative framing | Perplexity Sonar | sonar | 2026-09-17 13:21 | yes | 20 | 7 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 10:07 | yes | 22 | 15 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 09:50 | yes | 5 | 5 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 13:15 | yes | 30 | 31 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 10:46 | yes | 5 | 4 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 12:55 | no | 0 | 46 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 12:10 | yes | 26 | 38 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 12:56 | yes | 28 | 104 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 10:12 | yes | 19 | 72 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
Astra read as DataStax Astra DB
DataStax Astra (Apache Cassandra) read as DataStax Astra DB
MongoDB Atlas read as MongoDB
Oracle NoSQL read as Oracle NoSQL Database
Oracle NoSQL Cloud read as Oracle NoSQL Database
Riak read as Riak KV
Unresolved, counted raw
ActorDB
Avro / Parquet
HanoiDB
Tokyo Cabinet
VAST
Discontinued, still offered
No shut-down product was recommended here.