NoSQL databases for enterprise buyers
Asked as “NoSQL database”, and as “document or key-value database service”, on behalf of an enterprise B2B company. 55 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
56%
Clear leader
56MongoDB16Amazon DynamoDB07Azure Cosmos DB20others
56% of first choices, clear leader.
Since September 2026=heldSince September 2026: 53% → 53%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.MongoDB held the lead, ±0 points on 53%, inside the 11-point floor.
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.
Small business
MongoDB69%Clear leader▼−11Since September 2026: 77% → 66%, −11 points. Past the 11-point floor: movement. Read over the models both editions asked.Standing →Mid-marketMongoDB74%Clear leader▲+14Since September 2026: 59% → 73%, +14 points. Past the 11-point floor: movement. Read over the models both editions asked.Standing →EnterpriseMongoDB56%Clear leader=heldSince September 2026: 53% → 53%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.This page
MongoDB69%Clear leader▼−11Since September 2026: 77% → 66%, −11 points. Past the 11-point floor: movement. Read over the models both editions asked.Standing →Mid-marketMongoDB74%Clear leader▲+14Since September 2026: 59% → 73%, +14 points. Past the 11-point floor: movement. Read over the models both editions asked.Standing →EnterpriseMongoDB56%Clear leader=heldSince September 2026: 53% → 53%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.This pageThe standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all fourteen models, for an enterprise B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince September 2026
01MongoDB56%23%75endorsed leader=heldSince September 2026: 53% → 53%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.53% → 53%
MongoDB56%23%75endorsed leader=heldSince September 2026: 53% → 53%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.53% → 53%02
Amazon DynamoDB16%7%57accepted challenger▼−7Since September 2026: 24% → 17%, −7 points. Inside the 11-point floor: within noise. Read over the models both editions asked.24% → 17%
Amazon DynamoDB16%7%57accepted challenger▼−7Since September 2026: 24% → 17%, −7 points. Inside the 11-point floor: within noise. Read over the models both editions asked.24% → 17%03
Azure Cosmos DB7%8%40accepted challenger▲+4Since September 2026: 4% → 9%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 9%
Azure Cosmos DB7%8%40accepted challenger▲+4Since September 2026: 4% → 9%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 9%04
Couchbase7%5%41accepted challenger▲+6Since September 2026: 2% → 9%, +6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 9%
Couchbase7%5%41accepted challenger▲+6Since September 2026: 2% → 9%, +6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 9%05
Apache Cassandra4%23%43accepted challenger▲+4Since September 2026: 0% → 4%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 4%
Apache Cassandra4%23%43accepted challenger▲+4Since September 2026: 0% → 4%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 4%06
ScyllaDB2%17%18accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%
ScyllaDB2%17%18accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%07
Oracle NoSQL Database2%0%11accepted challenger▼−2Since September 2026: 4% → 2%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 2%
Oracle NoSQL Database2%0%11accepted challenger▼−2Since September 2026: 4% → 2%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 2%08
Redis2%34%41criticized challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%
Redis2%34%41criticized challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%Show the one product at 0%, ordered by negative rate
09
Neo4j0%12%17accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Neo4j0%12%17accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured
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.
All twenty-one head-to-head pages: the top seven products, each against each
MongoDB vs ScyllaDBMongoDB vs PostgreSQLMongoDB vs Google Cloud FirestoreAmazon DynamoDB vs CouchbaseAmazon DynamoDB vs RedisAmazon DynamoDB vs ScyllaDBAmazon DynamoDB vs PostgreSQLAmazon DynamoDB vs Google Cloud FirestoreCouchbase vs RedisCouchbase vs ScyllaDBCouchbase vs PostgreSQLCouchbase vs Google Cloud FirestoreRedis vs ScyllaDBRedis vs PostgreSQLRedis vs Google Cloud FirestoreScyllaDB vs PostgreSQLScyllaDB vs Google Cloud FirestorePostgreSQL vs Google Cloud Firestore
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 →
010203040506070809Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative70%
Key
01MongoDB56%
MongoDB56%02Amazon DynamoDB16%
Amazon DynamoDB16%03Azure Cosmos DB7%
Azure Cosmos DB7%04Couchbase7%
Couchbase7%05Apache Cassandra4%
Apache Cassandra4%06ScyllaDB2%
ScyllaDB2%07Oracle NoSQL Database2%
Oracle NoSQL Database2%08Redis2%
Redis2%09Neo4j0%
Neo4j0%What they warned about
Seven of fourteen models held their first choice under the paraphrase. Claude Haiku 4.5, Grok 4.1 Fast, Mistral Small, DeepSeek V4 Flash, Qwen 3.7 Flash, 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.
MongoDB23%
17 of 75 labels negative · 14 of 14 models · 2 hard negative
“MongoDB switched to the SSPL in 2018. This creates legal ambiguity ... making the free community version increasingly insufficient for production.” Qwen 3.7 Flash, negative prompt
Redis34%
14 of 41 labels negative · 11 of 14 models · 4 hard negative
“Redis changed its license from open-source BSD to SSPL in March 2024, making it no longer truly open-source... Consider Valkey instead” Claude Haiku 4.5, negative prompt
Apache Cassandra23%
10 of 43 labels negative · 8 of 14 models
“Complex to operate, and Cassandra's eventual consistency + CQL query limitations make it a poor fit for transactional B2B applications.” DeepSeek V4 Flash, direct prompt
Apache CouchDB62%
5 of 8 labels negative · 5 of 14 models · 4 hard negative
“slow for writes at scale, has limited querying ... Rarely chosen for new enterprise projects.” Grok 4.1 Fast, negative prompt
What they cite
Citations exist only for the models that return a source list: fourteen of the fourteen in this edition, and all six flagship models on the expanded tier.
Sites the answers cite
76 of 84 answers in this category came back with a source list, from 14 of 14 models: citations where the model returns them, or the search results it consulted. 1045 links across 263 sites, every framing counted. Ranked by the number of answers carrying the site or page. 30 of the 252 answers across every segment cited this index's own page for the category; the method page measures whether that reading tilts an answer.
vendor site · Guideflow29 answers · 29 citations · 11 models
vendor site · MongoDB28 answers · 50 citations · 13 models
vendor site · Aerospike26 answers · 31 citations · 11 models
23 answers · 23 citations · 11 models
22 answers · 27 citations · 10 models
vendor site · ScyllaDB21 answers · 33 citations · 9 models
21 answers · 25 citations · 10 models
21 answers · 22 citations · 10 models
vendor site · FitGap20 answers · 29 citations · 10 models
vendor site · Couchbase19 answers · 26 citations · 10 models
19 answers · 19 citations · 11 models
17 answers · 23 citations · 9 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.
28 answers · 11 models
23 answers · 11 models
22 answers · 11 models
19 answers · 9 models
19 answers · 11 models
16 answers · 9 models
16 answers · 8 models
14 answers · 7 models
14 answers · 9 models
11 answers · 7 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 | 56% | 192 | 74,000 | 9,630 | 342,632 | $8,625 | 120 | 1 |
| Amazon aws.amazon.com Amazon DynamoDB 16% | 16% | 155 | 151,000,000 | 1,380,939 | 9,238,321 | $20,476 | 5 | |
| Azure parent site Azure Cosmos DB 7% | 7% | 66 | 301,000 | 47,904 | ||||
| Couchbase couchbase.com | 7% | 94 | 4,400 | 221 | 62,564 | $34 | 26 | |
| Apache Cassandra apache.org | 4% | 116 | 1,300 | 165 | 1,144,025 | 0 | ||
| ScyllaDB scylladb.com | 2% | 44 | 1,900 | 76 | 22,468 | 120 | 3 | |
| Oracle parent site Oracle NoSQL Database 2% | 2% | 11 | 450,000 | 66,611 | ||||
| Redis redis.io | 2% | 137 | 27,100 | 6,980 | 112,424 | 18 | ||
| Aerospike aerospike.com | 2% | 11 | 2,900 | 250 | 70,270 | $25 | 24 | 2 |
| DataStax Astra datastax.com DataStax Astra DB 2%, DataStax 0% | 2% | 13 | 50 | 1 | 3,387 |
Google's top ten for the three searches hold 14 sites; 4 of them are among the sites the models cited here (aerospike.com, dnsstuff.com, mongodb.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 DynamoDB, MongoDBChangedno first choiceAzure Cosmos DBno first choiceagainst: Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, MongoDB, Redis
Five alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Neo4j, RedisThree alternatives
Amazon DocumentDB, Google Cloud Firestore, RedisFive alternatives
Apache Cassandra, Azure Cosmos DB, Couchbase, MongoDB, RedisTwo alternatives
Aerospike, Oracle NoSQL DatabaseGPT-5.4 miniMongoDBMongoDBHeldno first choiceAmazon DynamoDBagainst: Couchbase, MongoDBAzure Cosmos DB, Couchbaseagainst: Amazon DynamoDB, Apache Cassandra, Redis
Two alternatives
Amazon DynamoDB, CouchbaseTwo alternatives
Amazon DynamoDB, Azure Cosmos DBOne alternative
Azure Cosmos DBGemini 3.5 FlashMongoDBMongoDBHeldMongoDBCouchbaseagainst: Amazon DynamoDB On-Demand, Azure Cosmos DB Serverlessno first choiceagainst: Apache Cassandra, Apache HBase, Apache Voldemort, Hypertable, MongoDB, OrientDB, Redis, RethinkDB, Riak KV
Five alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase, DataStaxThree alternatives
Amazon DynamoDB, Azure Cosmos DB, RedisSix alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, DataStax, Neo4j, RedisTwo alternatives
MongoDB, ScyllaDBPerplexity SonarMongoDBMongoDBHeldno first choiceOracle NoSQL Databaseagainst: MongoDBno first choicenothing named
Five alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase, ScyllaDBTwo alternatives
Couchbase, IBM CloudantTwo alternatives
Aerospike, Google Cloud BigtableGrok 4.1 FastMongoDBAmazon DynamoDB, MongoDBChangedagainst: RedisMongoDBScyllaDBagainst: Amazon DynamoDB, MongoDBAmazon DynamoDB, DataStax Astra DB, MongoDBagainst: Apache Cassandra, Redisagainst: Apache CouchDB, HyperDex, MongoDB, Redis, Riak KV
Three alternatives
Amazon DynamoDB, Apache Cassandra, RedisTwo alternatives
Amazon DocumentDB, CouchbaseSix alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase, Neo4j, RedisTwo alternatives
Aerospike, Apache CassandraOne alternative
CouchbaseMistral SmallMongoDBAmazon DynamoDB, MongoDBChangedno first choiceAmazon DynamoDBagainst: Apache Cassandrano first choiceagainst: MongoDB
Three alternatives
Aerospike, Apache Cassandra, RedisSeven alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase, Google Cloud Bigtable, MongoDB, Oracle NoSQL DatabaseOne alternative
MongoDBDeepSeek V4 FlashMongoDBagainst: Apache CassandraAmazon DynamoDB, MongoDBChangedagainst: Aerospikeno first choiceMongoDBagainst: Apache Cassandra, Azure Cosmos DB, ScyllaDBno first choiceagainst: Apache Cassandraagainst: Apache CouchDB, Apache HBase, LevelDB/RocksDB, MongoDB, Neo4j, OrientDB, Riak KV
Three alternatives
Amazon DynamoDB, Redis, ScyllaDBThree alternatives
Azure Cosmos DB, Couchbase, RedisTwo alternatives
Amazon DynamoDB, Oracle NoSQL DatabaseLlama 4 Maverickno first choiceno first choiceHeldno first choiceno first choiceno first choiceagainst: Apache CouchDB, MongoDB, Redis
Qwen 3.7 FlashApache Cassandra, MongoDBMongoDBChangedApache Cassandra, Azure Cosmos DB, Couchbase, DataStax, MongoDB, RedisApache Cassandraagainst: AWS DynamoDB On-Demand, Azure Cosmos DB, Google Cloud Firestoreno first choiceagainst: Apache Cassandra, Apache CouchDB, Apache HBase, Elasticsearch, MongoDB, Neo4j, Redis
Two alternatives
Neo4j, RedisTwo alternatives
Amazon DynamoDB, Azure Cosmos DBOne alternative
Neo4jFour alternatives
CockroachDB, Couchbase, MongoDB, YugabyteDBKimi K2MongoDBMongoDB, RedisChangedagainst: Amazon ElastiCacheno first choiceCouchbaseagainst: MongoDBAmazon DynamoDB, Azure Cosmos DB, MongoDBagainst: Apache CouchDB, Apache HBase, Redis, Riak KV, Tokyo Cabinet/Kyoto Cabinet, Voldemort
Two alternatives
Apache Cassandra, RedisTwo alternatives
Amazon DynamoDB, Azure Cosmos DBThree alternatives
CockroachDB, DataStax, PostgreSQLGLM 4.7 FlashXMongoDBMongoDBHeldMongoDBCouchbaseagainst: MongoDBno first choiceagainst: Apache Cassandra, Apache HBase, MongoDB, Redis
Six alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase, DataStax Astra DB, RedisOne alternative
Amazon DynamoDBThree alternatives
Apache Cassandra, Neo4j, RedisMiniMax M2.5Azure Cosmos DB, MongoDBMongoDBChangedMongoDBno first choiceno first choiceagainst: MongoDB, Redis, ScyllaDB
Seven alternatives
Amazon DocumentDB, Amazon DynamoDB, Apache Cassandra, Couchbase, Neo4j, Redis, ScyllaDBFour alternatives
Amazon DocumentDB, Amazon DynamoDB, Couchbase, IBM CloudantFive alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Google Cloud Bigtable, ScyllaDBGPT-6 LunaMongoDBMongoDBHeldno first choiceAmazon DynamoDBMongoDBagainst: Amazon DynamoDB, Google Cloud Firestore, MongoDB, Redis, RethinkDB
Two alternatives
Amazon DynamoDB, Azure Cosmos DBTwo alternatives
Amazon DynamoDB, Azure Cosmos DB for NoSQLOne alternative
ScyllaDBThree alternatives
Amazon DynamoDB, Azure Cosmos DB, Google Cloud FirestoreMuse Glimmer 30BMongoDBMongoDBHeldno first choiceAerospikeMongoDBagainst: Couchbase, DataStax, Elasticsearch, MongoDB, Redis, ScyllaDB
Eight alternatives
Aerospike, Amazon DocumentDB, Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase, DataStax Astra DB, RedisSeven alternatives
Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase, MongoDB, Neo4j, RedisTwo alternatives
Oracle NoSQL Database, ScyllaDBThree alternatives
Amazon DocumentDB, Amazon DynamoDB, Azure Cosmos DBBold 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. Full answer text is in the free responses file. Download the record
Eighty-four 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 | 2026-10-01 13:31 | no | 0 | 4 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 07:40 | yes | 2 | 4 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:29 | yes | 7 | 35 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 08:58 | yes | 14 | 6 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:37 | yes | 20 | 7 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 07:48 | yes | 5 | 3 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:13 | yes | 20 | 32 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:13 | yes | 5 | 1 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:51 | yes | 8 | 40 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:47 | yes | 9 | 17 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:00 | yes | 24 | 89 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:05 | no | 0 | 42 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:25 | yes | 3 | 10 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:38 | yes | 13 | 28 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 12:42 | no | 0 | 5 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:23 | yes | 3 | 5 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 09:51 | no | 0 | 13 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 08:44 | yes | 16 | 3 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:57 | yes | 19 | 7 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:59 | yes | 10 | 5 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 12:00 | yes | 25 | 28 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 12:35 | yes | 5 | 1 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:51 | yes | 10 | 22 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:02 | yes | 20 | 21 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:43 | yes | 15 | 48 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:36 | yes | 10 | 55 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 12:22 | yes | 3 | 10 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:37 | yes | 12 | 10 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 12:15 | yes | 9 | 8 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:37 | yes | 7 | 7 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:44 | no | 0 | 20 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 12:20 | yes | 19 | 6 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:25 | yes | 24 | 7 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:06 | yes | 13 | 11 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:45 | yes | 19 | 57 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 13:23 | yes | 5 | 1 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:23 | yes | 5 | 17 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 07:46 | yes | 15 | 28 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 13:29 | yes | 25 | 175 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 12:39 | yes | 9 | 20 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:06 | yes | 8 | 19 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:58 | yes | 21 | 41 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:19 | yes | 17 | 8 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 08:31 | yes | 5 | 4 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:02 | yes | 23 | 29 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 12:52 | yes | 20 | 3 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 13:48 | yes | 23 | 8 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:20 | yes | 20 | 6 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:53 | yes | 22 | 19 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 13:24 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 07:42 | no | 0 | 42 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:47 | yes | 24 | 30 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:58 | yes | 22 | 27 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 13:33 | yes | 15 | 75 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 12:06 | yes | 2 | 15 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:01 | yes | 14 | 23 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:00 | yes | 19 | 11 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:59 | yes | 6 | 11 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:05 | yes | 9 | 24 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 10:58 | yes | 20 | 6 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:19 | yes | 13 | 8 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:49 | no | 0 | 7 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:05 | yes | 23 | 35 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 11:34 | yes | 5 | 2 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 13:28 | no | 0 | 28 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:43 | yes | 25 | 28 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:53 | yes | 14 | 28 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:04 | yes | 5 | 48 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 11:07 | yes | 5 | 17 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 11:44 | yes | 15 | 25 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 13:03 | yes | 17 | 11 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:15 | yes | 6 | 7 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:33 | yes | 17 | 28 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 09:28 | yes | 20 | 5 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:06 | yes | 24 | 10 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:27 | yes | 5 | 4 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:13 | yes | 22 | 46 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:47 | yes | 5 | 1 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:19 | yes | 15 | 63 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:23 | yes | 24 | 30 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:39 | yes | 25 | 88 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:48 | yes | 10 | 32 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 11:36 | yes | 4 | 24 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:22 | yes | 23 | 32 s |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
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Category-scoped readings
DataStax Astra read as DataStax Astra DB
MongoDB Atlas read as MongoDB
MongoDB Atlas (Dedicated Tiers) read as MongoDB
Oracle NoSQL read as Oracle NoSQL Database
Riak read as Riak KV
Unresolved, counted raw
AWS DynamoDB On-Demand
AWS Neptune
Amazon DynamoDB On-Demand
Amazon ElastiCache for Redis
Apache Voldemort
Azure Cosmos DB Serverless
HyperDex
LevelDB/RocksDB
PostgreSQL's JSONB
Tokyo Cabinet/Kyoto Cabinet
Discontinued, still offered
No shut-down product was recommended here.