MemVerdict

Pilot results: 30 of 500 questions per system. Gaps of a few points are within noise; 95% intervals are shown. The full run is in progress.

Full context vs Mem0: accuracy, cost and latency

A memory system against a no-memory baseline: does the memory layer earn its cost? Independent results on the same 30 questions of LongMemEval-S (cleaned, 2025-09), with the same reader model and the official judge. Pilot sample, so accuracy is shown with 95% intervals.

Full context

Baseline, not a memory system
Vendor
Baseline
Version
-
License
-
Result
83.3% (95% CI 66–93%)

Mem0

Memory system
Vendor
Mem0
Version
2.2.1 (open source)
License
Apache-2.0
Result
83.3% (95% CI 66–93%)

Verdict

Generated from the pilot data, n=30 per system

Is a memory layer worth it here?

Not demonstrated in this pilot: Mem0 and the Full context baseline are statistically tied on accuracy at n=30 (Mem0 83.3% vs Full context 83.3%; the 95% intervals overlap), and Mem0 costs 7.1× more per 1,000 questions. The reader sees a median of 50k characters of context per question with Mem0 and 500k with Full context.

  • AccuracyFull context and Mem0 are tied on accuracy: both answered 25 of 30 questions correctly (83.3%, 95% CI 66–93%).
  • CostFull context costs 7.1× less per 1,000 questions: $13.1 vs $93.0 for Mem0.
  • LatencySimilar latency: median 4.8s for Full context vs 4.3s for Mem0 per question (p90 8.0s vs 7.4s).
  • IngestionThe Full context baseline needs no ingestion step; Mem0 takes 15.2 min to ingest one chat history (median).

Key numbers side by side

Bold marks the better value. Accuracy rows are not bolded when the 95% intervals overlap.

Key numbers for Full context and Mem0
MetricFull contextMem0
Accuracy (official judge)83.3%83.3%
95% interval66–93%66–93%
Panel accuracy86.7%83.3%
Cost per 1,000 questions$13.1$93.0
of which memory side$0.00$91.6
of which answering$13.1$1.32
Latency p504.8s4.3s
Latency p908.0s7.4s
Ingestion per history (median)none15.2 min
Context given to reader (median chars)500k50k

Full context and Mem0 among all tested systems

Full context and Mem0 are highlighted; other systems are muted for context, baselines lightest. Each name links to the system's page.

Accuracy

Higher is better

Share of 30 questions answered correctly (official judge). Whiskers: 95% interval.

  1. Cognee
    90.0%74–97%
  2. Hindsight
    90.0%74–97%
  3. Plain RAG
    86.7%70–95%
  4. Mem0
    83.3%66–93%
  5. Full context
    83.3%66–93%
  6. LangMem
    33.3%19–51%

Not shown: Graphiti (pilot result withdrawn, rerun in progress).

Cost per 1,000 questions

Lower is better

USD billed (prices as of 2026-10-08). Solid: memory side (ingestion and retrieval). Light: answering.

  1. Plain RAG
    $5.45
  2. Full context
    $13.1
  3. LangMem
    $61.6
  4. Mem0
    $93.0
  5. Cognee
    $141
  6. Hindsight
    $172

Not shown: Graphiti (pilot result withdrawn, rerun in progress).

Latency per question

Lower is better

Retrieval plus answer, in seconds. Solid: median (p50). Light: up to p90.

  1. LangMem
    3.5sp90 5.3s
  2. Cognee
    4.0sp90 8.1s
  3. Mem0
    4.3sp90 7.4s
  4. Full context
    4.8sp90 8.0s
  5. Plain RAG
    4.8sp90 7.4s
  6. Hindsight
    5.4sp90 8.1s

Not shown: Graphiti (pilot result withdrawn, rerun in progress).

Ingestion time

Lower is better

Median time to load one question's chat history into the system.

  1. Full context
    none
  2. Plain RAG
    1s
  3. Cognee
    2.8 min
  4. LangMem
    8.5 min
  5. Hindsight
    8.8 min
  6. Mem0
    15.2 min

Not shown: Graphiti (pilot result withdrawn, rerun in progress).

Accuracy by question type

Difference is Full context minus Mem0, in percentage points. Each type has only a few pilot questions (n), so one question can move a row by 13 points or more.

Accuracy by question type, Full context vs Mem0
Question typenFull contextMem0Difference
Single-session (user)4100%75%+25 pts in favour of Full context
Single-session (assistant)3100%100%0
Preferences2100%100%0
Multi-session875%75%0
Knowledge update4100%100%0
Temporal reasoning875%88%−13 pts in favour of Mem0
Abstention10%0%0

What each one is

Full context

No memory system. The entire chat history (about 100k tokens) is placed in the reader's prompt.

How we ran it

Official LongMemEval long-context setting with the official answer prompt.

All Full context results

Mem0

Extracts short memories from each exchange with an LLM, stores them in a vector store, and retrieves them with semantic, keyword and entity signals.

How we ran it

Open-source SDK with local Qdrant, NLP extras installed (spaCy, BM25). Mirrors Mem0's own LongMemEval harness: one add() per user+assistant pair, top_k=200 at search. Historical timestamps are platform-only, so the shared clock simulation supplies dates.

All Mem0 resultsSource repository

What the vendors report

Mem0

LongMemEval scores reported for Mem0
ScoreVariantReaderJudgeNoteSource
83.3%S (cleaned, 2025-09), 30-question pilotopenai/gpt-6-lunagpt-4o-2024-08-06, official promptMemVerdict measurement, Mem0 2.2.1 (open source)This page
94.4%SGPT-5GPT-5 with Mem0's own lenient promptManaged platform, not the open-source SDK.mem0.ai/research

Vendor-reported scores and ours are measured differently, so a gap does not by itself mean either number is wrong. Our pilot uses openai/gpt-6-luna as the reader that writes each answer, gpt-4o-2024-08-06 with the official LongMemEval judge prompt, LongMemEval-S (cleaned, 2025-09) and 30 questions, with each system set up as described above. Scores move with the reader model, the judge model and its prompt, the dataset version, the number of questions, and whether a hosted platform or the open-source package is tested.

Frequently asked questions

Is Full context better than Mem0?

Full context and Mem0 are tied on accuracy: both answered 25 of 30 questions correctly (83.3%, 95% CI 66–93%). With the 3-judge panel majority instead of the official judge: Full context 86.7%, Mem0 83.3%. No question type separates them by more than one question (each question type has only 1 to 8 questions in this pilot). These are pilot numbers (30 questions each); the full 500-question run will narrow the intervals.

Which is cheaper, Full context or Mem0?

Full context costs 7.1× less per 1,000 questions: $13.1 vs $93.0 for Mem0. Full context: $13.1 per 1,000 questions, all of it answering (no memory-side cost). Mem0: $93.0 per 1,000 questions, of which $91.6 (99%) is memory-side (ingestion and retrieval) and $1.32 is answering. Prices as of 2026-10-08.

Which is faster, Full context or Mem0?

Similar latency: median 4.8s for Full context vs 4.3s for Mem0 per question (p90 8.0s vs 7.4s). The Full context baseline needs no ingestion step; Mem0 takes 15.2 min to ingest one chat history (median). Latency is measured per question as retrieval plus answering; ingestion is the time to load one question's chat history.

How were Full context and Mem0 tested?

Both were run on the same 30 questions of LongMemEval-S (cleaned, 2025-09) as every other system, by the same harness. For each question, the system ingests that question's chat history, then retrieves context that one reader model (openai/gpt-6-luna) uses to answer with the official LongMemEval prompt. Answers are graded by the official judge (gpt-4o-2024-08-06) and cross-checked by 3 other judges; all judges agreed on 95% of graded answers. Costs are what the providers billed (prices as of 2026-10-08). Full context: Official LongMemEval long-context setting with the official answer prompt. Mem0: Open-source SDK with local Qdrant, NLP extras installed (spaCy, BM25). Mirrors Mem0's own LongMemEval harness: one add() per user+assistant pair, top_k=200 at search. Historical timestamps are platform-only, so the shared clock simulation supplies dates.

Which should I use, Full context or Mem0?

Not demonstrated in this pilot: Mem0 and the Full context baseline are statistically tied on accuracy at n=30 (Mem0 83.3% vs Full context 83.3%; the 95% intervals overlap), and Mem0 costs 7.1× more per 1,000 questions. The reader sees a median of 50k characters of context per question with Mem0 and 500k with Full context. If the Full context baseline is as accurate on your own data, it is the simpler option to run; test both on a sample of your real conversations before committing. These are pilot numbers (30 questions each); the full 500-question run will narrow the intervals.

More comparisons

Other comparisons with Full context

Other comparisons with Mem0

All comparisons