Language modelActive

Gemini 2.5 Flash-Lite

Google

Released: July 22, 2025
Data date: September 8, 2026
Predecessor: Gemini 2.0 Flash-Lite Preview (Official documentation)
Successor: Gemini 3.1 Flash-Lite

Speed: 325.9 tokens/s
Output throughput
Price: $0.1 · $0.4
Input · Output / 1M tokens
Input: Text, Image, Audio, Video
Output: Text

What is new about Gemini 2.5 Flash-Lite is the combination of stable GA availability and a now-restricted access policy. Google continues serving the API to users who previously used the 2.5 models, while recommending newer models for new projects. The documented predecessor is Gemini 2.0 Flash-Lite Preview.

Google does not publish a parameter count. Its model card describes Gemini 2.5 Flash-Lite as a sparse mixture-of-experts model with native multimodal support for text, vision, and audio.

Specifications and access

Specification	Value and source
Weights	ProprietarySource
Access	APISource
Context window	1,048,576 tokensSource
Knowledge cutoff	January 2025Source
Notes	1M context, parameter count not disclosedSource
API model ID	gemini-2.5-flash-liteSource
Source status	stableSource
Maximum output	65,536 tokensSource
Input	Text, Image, Video, Audio, pdfSource
Output	TextSource
Reasoning mode	low, medium, highSource
Tool calling	YesSource
Structured output	YesSource
Release	2025-07-22Source
Knowledge cutoff	2025-01Source
Capabilities	function calling, structured outputs, search grounding, Google Maps grounding, URL context, code execution, file searchSource
General availability	2025-07-22 (General availability (GA).)Source

Published benchmarks

Health

MedCode

Overall

1.Claude Opus 563.57%

2.Gemini 3.1 Pro Preview59.06%

3.Claude Fable 556.07%

4.Gemini 3 Flash Preview55.92%

5.Gemini 3.5 Flash55.83%

6.Claude Opus 4.754.86%

7.Claude Fable 5.153.51%

8.Gemini 3.7 Flash53.39%

9.Claude Opus 4.853.22%

10.Gemini 3.6 Flash53.15%

11.Claude Sonnet 5.552.92%

12.Gemini 2.5 Pro50.59%

13.Claude Opus 5.549.8%

14.GPT-549.63%

15.Grok 4.749.55%

16.GPT-5.549.1%

17.Kimi K348.88%

18.GPT-6 Astra48.49%

19.Claude Opus 4.648.24%

20.Gemini 3.8 Flash48.14%

21.Gemini 3.1 Flash-Lite47.6%

22.Claude Sonnet 547.54%

23.o347.29%

24.GPT-6 Sol47.07%

25.MiniMax M346.29%

26.Claude Opus 4.545.17%

27.Grok 4.644.71%

28.GPT-6 Luna44.69%

29.GPT-5.6 Sol43.97%

30.Gemini 3.5 Flash-Lite43.49%

31.GPT-5.6 Terra43.41%

32.Grok 4.543.29%

33.GPT-5 mini43.05%

34.GLM-5.342.86%

35.GPT-5.6 Luna42.39%

36.GLM-5.141.6%

37.GPT-5.441.29%

38.Inkling41.19%

39.DeepSeek-V4.1-Flash41.17%

40.GPT-5.4 nano41.03%

41.GLM-5.240.77%

42.Qwen 3.8 Max 090240.67%

43.Claude Sonnet 4.540.57%

44.DeepSeek-V4-Pro40.46%

45.Kimi K2.640.14%

46.Kimi K2.539.32%

47.Qwen 3.7 Max38.75%

48.Nemotron 3 Ultra38.62%

49.Grok 4.338.07%

50.Inkling Small37.89%

51.MiniMax M2.734.44%

52.MiniMax M2.134.08%

53.o4-mini33.79%

54.Mistral Medium 3.533.75%

55.GLM-4.732.77%

56.MiMo-V2.5-Pro32.48%

57.Ling 3.0 Flash32.28%

58.Grok 4.20 Reasoning32.16%

59.MiMo-V2.531.9%

60.Mercury 2.531.33%

61.GPT-5 nano30.44%

62.Qwen 3.8 27B28.7%

63.Gemini 2.5 Flash-Lite27.12%

64.Llama 4 Scout23.31%

higher is betterAs of September 29, 2026Scale: 0% - 100%

Source: Vals AI

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: vals-medcode:1:overall
Metric: accuracy
Scale: 0-100
Participants: 64
Methodology and context: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-26. Latency in seconds: 5.746. Cost per task in USD: 0.001342. Source ID: vals-ai. Evidence capture ID: vals-medcode-2026-09-29-7062229b5ab4. Source payload hash: 7062229b5ab4a5e16f014a78fd550f9f34e7147be6be796b610ab1ca3de055ab. Parser version: vals-astro-v2. Imported at: 2026-09-29

Source: Vals AI

MedScribe

Overall

1.Claude Opus 5.591.43%

2.Claude Fable 5.191.29%

3.Claude Sonnet 5.591.1%

4.Claude Opus 590.99%

5.Grok 4.789.38%

6.GLM-5.3-Flash88.94%

7.GLM-5.388.81%

8.Claude Fable 588.52%

9.Kimi K387.96%

10.GPT-6 Astra87.91%

11.MiniMax M387.25%

12.Grok 4.586.88%

13.GPT-5.586.87%

14.Claude Opus 4.686.74%

15.Grok 4.686.53%

16.Claude Opus 4.885.76%

17.DeepSeek-V4.1-Flash85.5%

18.Inkling85.41%

19.GPT-5.6 Sol85.23%

20.Qwen 3.8 Max 090284.95%

21.Claude Sonnet 4.584.52%

22.Gemini 3.8 Flash84.5%

23.GPT-5.6 Luna84.39%

24.Inkling Small84.11%

25.Gemini 3.7 Flash83.94%

26.Qwen 3.8 27B83.85%

27.MiMo-V2.5-Pro83.73%

28.GPT-6 Luna83.71%

29.GPT-583.65%

30.GLM-5.283.53%

31.Claude Opus 4.583.25%

32.Claude Opus 4.782.95%

33.GPT-5.6 Terra82.87%

34.GPT-6 Sol82.03%

35.Ling 3.0 Flash80.9%

36.MiniMax M2.180.78%

37.GPT-5 mini80.58%

38.MiniMax M2.779.87%

39.Gemini 3.6 Flash79.66%

40.Qwen 3.7 Max79.4%

41.Kimi K2.678.15%

42.GPT-5.477.55%

43.GPT-5.4 nano77.09%

44.o376.65%

45.Gemini 3.5 Flash76.57%

46.Kimi K2.576.44%

47.Gemini 3.1 Pro Preview76.11%

48.Claude Sonnet 576.05%

49.DeepSeek-V4-Pro75.14%

50.Grok 4.374.4%

51.Gemini 2.5 Pro73.55%

52.GPT-5 nano72.87%

53.Gemini 2.5 Flash-Lite72.83%

54.GLM-5.172.27%

55.MiMo-V2.572.15%

56.Gemini 3.5 Flash-Lite70.89%

57.Gemini 3 Flash Preview69.92%

58.o4-mini69.14%

59.GLM-4.768.63%

60.Mistral Medium 3.567.73%

61.Gemini 3.1 Flash-Lite63.9%

62.Grok 4.20 Reasoning63.41%

63.Mercury 2.555.09%

64.Llama 4 Scout50.59%

65.Nemotron 3.5 Lightning4.27%

higher is betterAs of September 29, 2026Scale: 0% - 100%

Source: Vals AI

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: vals-medscribe:1:overall
Metric: accuracy
Scale: 0-100
Participants: 65
Methodology and context: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-26. Latency in seconds: 4.847. Cost per task in USD: 0.001211. Source ID: vals-ai. Evidence capture ID: vals-medscribe-2026-09-29-6533d95c8055. Source payload hash: 6533d95c80553d2dd26927f7269ab6454c589d2181f1858c662a4f493b5685c7. Parser version: vals-astro-v2. Imported at: 2026-09-29

Source: Vals AI

Education

SAGE

Sub-task	Gemini 2.5 Flash-Lite (This model)	Kimi K3	Gemini 2.5 Pro	Claude Opus 5
Automata	33.33%	44.05% (best value in this row)	43.65%	32.54%
Calculus	13.89%	75.62%	70.68%	77.16% (best value in this row)
Linear Algebra	28.63%	72.7%	51.94%	76.06% (best value in this row)
Partial Differential Equations	20.64%	46.03% (best value in this row)	30.16%	39.68%

higher is betterAs of August 11, 2026Source: Vals AIbars scaled within each row, not across rows

Methodology and context

Metric: accuracy

SAGE

Overall

1.Claude Opus 4.756.1%

2.Gemma 4 31B55.03%

3.Claude Opus 4.854.79%

4.Kimi K354.26%

5.GPT-5.6 Sol52.56%

6.Qwen 3.8 27B52.4%

7.Claude Fable 551.89%

8.Gemini 3 Flash Preview51.85%

9.Claude Sonnet 5.551.77%

10.GPT-5.551.53%

11.Qwen 3.8 Max 090251.25%

12.GPT-5.4 mini50.81%

13.MiniMax M350.57%

14.Kimi K2.650.22%

15.Gemini 3.5 Flash49.89%

16.Kimi K2.549.87%

17.Gemini 3.1 Flash-Lite49.54%

18.Claude Opus 549.43%

19.Gemini 3.7 Flash49.23%

20.Claude Sonnet 548.93%

21.Gemini 3.6 Flash48.69%

22.Gemini 3.1 Pro Preview48.68%

23.Claude Fable 5.148.53%

24.GPT-6 Luna48.09%

25.DeepSeek-V4.1-Flash47.88%

26.Gemini 3.5 Flash-Lite47.3%

27.GPT-5.6 Terra47%

28.Claude Sonnet 4.646.58%

29.GPT-6 Astra46.37%

30.Claude Opus 5.545.83%

31.Qwen 3.6 27B45.58%

32.Claude Opus 4.545%

33.GPT-6 Sol44.79%

34.GPT-5.6 Luna44.22%

35.GPT-543.68%

36.GPT-5.443.31%

37.MiMo-V2.543.27%

38.GPT-5 mini42.99%

39.Gemini 2.5 Pro41.92%

40.o341.77%

41.o4-mini41.06%

42.Grok 4.740.79%

43.Qwen 3.7 Plus39.25%

44.Grok 4.20 Reasoning38.24%

45.GPT-5.4 nano38.08%

46.Mistral Medium 3.537.61%

47.Inkling36.56%

48.Gemini 3.8 Flash35.06%

49.Grok 4.534.97%

50.Llama 4 Scout34.83%

51.Claude Sonnet 4.532.88%

52.Inkling Small32.74%

53.GPT-5 nano30.38%

54.Grok 4.628.9%

55.Gemini 2.5 Flash-Lite27.53%

56.Mistral Large 324.6%

57.Grok 4.319.74%

higher is betterAs of September 29, 2026Scale: 0% - 100%

Source: Vals AI

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: vals-sage:1:overall
Metric: accuracy
Scale: 0-100
Participants: 57
Methodology and context: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-26. Latency in seconds: 13.781. Cost per task in USD: 0.000929. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-09-29-89f30a3444b7. Source payload hash: 89f30a3444b7edab27c895a52e2ce3759c99ee458f9763715a7a344cb2456193. Parser version: vals-astro-v2. Imported at: 2026-09-29

Source: Vals AI

Measurements without matching peer values

These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.

GPQA (Show measurement, test conditions, and source)

Source value: 66.7
Score: 66.7
Metric: percent
Unit: %
Category: vendor-evaluation
Direction: Higher is better
Source type: vendor-reported
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Thinking configuration

Source Google DeepMind

AIME (Show measurement, test conditions, and source)

Source value: 63.1
Score: 63.1
Metric: percent
Unit: %
Category: vendor-evaluation
Direction: Higher is better
Source type: vendor-reported
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Thinking configuration

Source Google DeepMind

LiveCodeBench (Show measurement, test conditions, and source)

Source value: 34.3
Score: 34.3
Metric: percent
Unit: %
Category: vendor-evaluation
Direction: Higher is better
Source type: vendor-reported
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Thinking configuration

Source Google DeepMind

SWE-bench Verified (single) (Show measurement, test conditions, and source)

Source value: 27.6
Score: 27.6
Metric: percent
Unit: %
Category: vendor-evaluation
Direction: Higher is better
Source type: vendor-reported
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Thinking configuration; single-agent

Source Google DeepMind

SWE-bench Verified (multi) (Show measurement, test conditions, and source)

Source value: 44.9
Score: 44.9
Metric: percent
Unit: %
Category: vendor-evaluation
Direction: Higher is better
Source type: vendor-reported
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Thinking configuration; multi-agent

Source Google DeepMind

Recorded measurement details

MedCode: Overall (Show measurement, test conditions, and source)

Source value: 27.115
Score: 27.115
Metric: accuracy
Unit: %
Benchmark version: 1
Category: health
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-medcode:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-09-29
Methodology: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-26. Latency in seconds: 5.746. Cost per task in USD: 0.001342. Source ID: vals-ai. Evidence capture ID: vals-medcode-2026-09-29-7062229b5ab4. Source payload hash: 7062229b5ab4a5e16f014a78fd550f9f34e7147be6be796b610ab1ca3de055ab. Parser version: vals-astro-v2. Imported at: 2026-09-29

Source Vals AI

MedScribe: Overall (Show measurement, test conditions, and source)

Source value: 72.832
Score: 72.832
Metric: accuracy
Unit: %
Benchmark version: 1
Category: health
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-medscribe:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-09-29
Methodology: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-26. Latency in seconds: 4.847. Cost per task in USD: 0.001211. Source ID: vals-ai. Evidence capture ID: vals-medscribe-2026-09-29-6533d95c8055. Source payload hash: 6533d95c80553d2dd26927f7269ab6454c589d2181f1858c662a4f493b5685c7. Parser version: vals-astro-v2. Imported at: 2026-09-29

Source Vals AI

SAGE: Automata (Show measurement, test conditions, and source)

Source value: 33.334
Score: 33.334
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: automata
Task label: Automata
Direction: Higher is better
Comparison cohort: vals-sage:1:automata
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-08-11
Methodology: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Provider reported by source: Google. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-09. Temperature: 1. Maximum output tokens: 30000. Latency in seconds: 10.448. Standard error: 6.023. Cost per task in USD: 0.000876. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-08-11-f1f79f6d8af3. Source payload hash: f1f79f6d8af3af8a952baa6793df3cdbd11ccfc53c0b95683b89817df366a496. Parser version: vals-astro-v2. Imported at: 2026-08-11
Comparison methodology: Temperature: 1. Maximum output tokens: 30000

Source Vals AI

SAGE: Calculus (Show measurement, test conditions, and source)

Source value: 13.889
Score: 13.889
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: calculus
Task label: Calculus
Direction: Higher is better
Comparison cohort: vals-sage:1:calculus
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-08-11
Methodology: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Provider reported by source: Google. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-09. Temperature: 1. Maximum output tokens: 30000. Latency in seconds: 13.337. Standard error: 4.298. Cost per task in USD: 0.001699. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-08-11-f1f79f6d8af3. Source payload hash: f1f79f6d8af3af8a952baa6793df3cdbd11ccfc53c0b95683b89817df366a496. Parser version: vals-astro-v2. Imported at: 2026-08-11
Comparison methodology: Temperature: 1. Maximum output tokens: 30000

Source Vals AI

SAGE: Linear Algebra (Show measurement, test conditions, and source)

Source value: 28.629
Score: 28.629
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: linear_algebra
Task label: Linear Algebra
Direction: Higher is better
Comparison cohort: vals-sage:1:linear_algebra
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-08-11
Methodology: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Provider reported by source: Google. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-09. Temperature: 1. Maximum output tokens: 30000. Latency in seconds: 18.12. Standard error: 5.077. Cost per task in USD: 0.000791. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-08-11-f1f79f6d8af3. Source payload hash: f1f79f6d8af3af8a952baa6793df3cdbd11ccfc53c0b95683b89817df366a496. Parser version: vals-astro-v2. Imported at: 2026-08-11
Comparison methodology: Temperature: 1. Maximum output tokens: 30000

Source Vals AI

SAGE: Partial Differential Equations (Show measurement, test conditions, and source)

Source value: 20.635
Score: 20.635
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: PDEs
Task label: Partial Differential Equations
Direction: Higher is better
Comparison cohort: vals-sage:1:PDEs
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-08-11
Methodology: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Provider reported by source: Google. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-09. Temperature: 1. Maximum output tokens: 30000. Latency in seconds: 12.776. Standard error: 5.078. Cost per task in USD: 0.001121. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-08-11-f1f79f6d8af3. Source payload hash: f1f79f6d8af3af8a952baa6793df3cdbd11ccfc53c0b95683b89817df366a496. Parser version: vals-astro-v2. Imported at: 2026-08-11
Comparison methodology: Temperature: 1. Maximum output tokens: 30000

Source Vals AI

SAGE: Overall (Show measurement, test conditions, and source)

Source value: 27.533
Score: 27.533
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-sage:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-09-29
Methodology: Source model ID: google/gemini-2.5-flash-lite. Source model name: Gemini 2.5 Flash-Lite. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-26. Latency in seconds: 13.781. Cost per task in USD: 0.000929. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-09-29-89f30a3444b7. Source payload hash: 89f30a3444b7edab27c895a52e2ce3759c99ee458f9763715a7a344cb2456193. Parser version: vals-astro-v2. Imported at: 2026-09-29

Source Vals AI

Page 1 of 3

Predecessor and successor

Predecessor: Gemini 2.0 Flash-Lite Preview (Official documentation)
Official documentation
Model lineage evidence
The lifecycle table lists `gemini-2.0-flash-lite-preview` (and its dated snapshot `gemini-2.0-flash-lite-preview-02-05`) as discontinued December 9, 2025 with recommended replacement `gemini-2.5-flash-lite`.
Google Gemini API model deprecations and lifecycle table (2026-09-19)
Later model generation: Gemini 3.1 Flash-Lite
Next generation in the directory
Model lineage evidence
The table gives the GA `gemini-2.5-flash-lite` id itself no shutdown date or replacement (still active); only its dated preview snapshot `gemini-2.5-flash-lite-preview-09-2025` is marked replaced by `gemini-3.1-flash-lite`, so this Flash-Lite-tier link is recorded as nearest-profile rather than a directly documented GA-to-GA succession.
Google Gemini API model deprecations and lifecycle table (2026-09-19)

Sources and data date

Type	Evidence and data date
Speed	Google, Non-reasoning. Output speed after the first streaming chunk, not total response time. Classified as deprecated by Artificial Analysis. Artificial Analysis maintains only the default 10K-input workload there. Retrieved September 8, 2026. Source: Artificial Analysis · Artificial Analysis (retrieved September 8, 2026)Speed measurement methodology
Price	Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs. Source: Pricing · API pricing
Input	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Context window data (retrieved September 8, 2026) · Gemini 2.5 Flash-Lite API documentation · Editorial description reviewed October 2, 2026
Output	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Context window data (retrieved September 8, 2026) · Gemini 2.5 Flash-Lite API documentation · Editorial description reviewed October 2, 2026
Research status	Research date September 8, 2026. 4 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Not found in the inspected sources	parameters-billions Original wording Google does not publish a parameter count on the Gemini API model page.
Not found in the inspected sources	active-parameters-billions Original wording Google does not publish active parameter counts for Gemini API models.
Additional source	Model metadata
Additional source	Knowledge cutoff data
Additional source	Vals AI (retrieved September 29, 2026)
Additional source	Vals AI (retrieved September 29, 2026)
Additional source	Vals AI (retrieved August 11, 2026; September 29, 2026)
Additional source	Google DeepMind (retrieved September 8, 2026) · Gemini 2.5 Flash-Lite model card · Editorial description reviewed October 2, 2026
Additional source	Model metadata (retrieved September 8, 2026)
Additional source	OpenRouter: Popularity index (derived), CC BY 4.0
Additional source	Vercel AI Gateway: Popularity index (derived), CC BY 4.0
Additional source	Creative Commons Attribution 4.0

Gemini 2.5 Flash-Lite

Specifications and access

Published benchmarks

Health

MedCode

MedScribe

Education

SAGE

SAGE

Measurements without matching peer values

Recorded measurement details

Our tests

Pelican on a bicycle

Popularity

Published prices

Predecessor and successor

Sources and data date