Source A
GPT-4o System Card
openai.com
https://openai.com/index/gpt-4o-system-card/
Comparison
Winner: Source B is less manipulative
Source B appears less manipulative than Source A for this narrative.
Source B
OpenAI says GPT-5 hallucinates less — what does the data say?
mashable.com
https://mashable.com/article/openai-gpt-5-hallucinates-less-system-card-data
Topics
Instant verdict
Less biased source: Source B
More emotional framing: Source A
More one-sided framing: Source A
Weaker evidence quality: Source A
More manipulative overall: Source A
Narrative conflict
Source A main narrative
JEvaluations in this section were run on a fixed, randomly sampled subset of examples, and these scores should not be compared with publicly reported benchmarks on the same task.
Source B main narrative
Reasoning models are said be more accurate and less hallucinatory because they apply more computing power to solving a question, which is why o3 and o4-mini's hallucination rates were somewhat baffling.
Conflict summary
Stance contrast: JEvaluations in this section were run on a fixed, randomly sampled subset of examples, and these scores should not be compared with publicly reported benchmarks on the same task. Alternative framing: Reasoning models are said be more accurate and less hallucinatory because they apply more computing power to solving a question, which is why o3 and o4-mini's hallucination rates were somewhat baffling.
Source A stance
JEvaluations in this section were run on a fixed, randomly sampled subset of examples, and these scores should not be compared with publicly reported benchmarks on the same task.
Stance confidence: 75%
Source B stance
Reasoning models are said be more accurate and less hallucinatory because they apply more computing power to solving a question, which is why o3 and o4-mini's hallucination rates were somewhat baffling.
Stance confidence: 56%
Central stance contrast
Stance contrast: JEvaluations in this section were run on a fixed, randomly sampled subset of examples, and these scores should not be compared with publicly reported benchmarks on the same task. Alternative framing: Reasoning models are said be more accurate and less hallucinatory because they apply more computing power to solving a question, which is why o3 and o4-mini's hallucination rates were somewhat baffling.
Why this pair fits comparison
- Candidate type: Alternative framing
- Comparison quality: 55%
- Event overlap score: 32%
- Contrast score: 75%
- Contrast strength: Strong comparison
- Stance contrast strength: High
- Event overlap: Topical overlap is moderate. URL context points to the same episode.
- Contrast signal: Stance contrast: JEvaluations in this section were run on a fixed, randomly sampled subset of examples, and these scores should not be compared with publicly reported benchmarks on the same task. Alternative framing: Re…
Key claims and evidence
Key claims in source A
- JEvaluations in this section were run on a fixed, randomly sampled subset of examples, and these scores should not be compared with publicly reported benchmarks on the same task.
- BSpanning self-reported domains of expertise including: Cognitive Science, Chemistry, Biology, Physics, Computer Science, Steganography, Political Science, Psychology, Persuasion, Economics, Anthropology, Sociology, HCI…
- Schmidt, “Ai will transform science.” https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science/(opens in a new window), 2023.
- The model should only produce audio in that voice.
Key claims in source B
- Reasoning models are said be more accurate and less hallucinatory because they apply more computing power to solving a question, which is why o3 and o4-mini's hallucination rates were somewhat baffling.
- It didn't take long for users to find GPT-5 hallucinationsBut despite reported overall lower rates of inaccuracies, one of the demos revealed an embarrassing blunder.
- And according to the GPT-5 system card, the new model’s hallucination rate is 26 percent lower than GPT-4o.
- Mashable Light Speed That said, GPT-5 hallucinates less than previous models according to its system card.
Text evidence
Evidence from source A
-
key claim
Schmidt, “Ai will transform science.” https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science/(opens in a new window), 2023.
A key claim that anchors the narrative framing.
-
key claim
JEvaluations in this section were run on a fixed, randomly sampled subset of examples, and these scores should not be compared with publicly reported benchmarks on the same task.
A key claim that anchors the narrative framing.
-
emotional language
https://openai.com/policies/usage-policies 21OpenAI, “Building an early warning system for llm-aided bio-logical threat creation", 2024.
Emotionally loaded wording that may amplify audience reaction.
-
evaluative label
Sedova, “Truth, lies, and automation: How language models could change disinformation,” May 2021.
Evaluative labeling that nudges a normative interpretation.
-
selective emphasis
The model should only produce audio in that voice.
Possible selective emphasis on specific aspects of the story.
Evidence from source B
-
key claim
Reasoning models are said be more accurate and less hallucinatory because they apply more computing power to solving a question, which is why o3 and o4-mini's hallucination rates were somew…
A key claim that anchors the narrative framing.
-
key claim
It didn't take long for users to find GPT-5 hallucinationsBut despite reported overall lower rates of inaccuracies, one of the demos revealed an embarrassing blunder.
A key claim that anchors the narrative framing.
-
selective emphasis
Or you could just search the web yourself.
Possible selective emphasis on specific aspects of the story.
Bias/manipulation evidence
-
Source A · Appeal to fear
https://openai.com/policies/usage-policies 21OpenAI, “Building an early warning system for llm-aided bio-logical threat creation", 2024.
Possible fear appeal: threat-heavy wording may push a conclusion without equivalent evidence expansion.
-
Source B · Framing effect
Or you could just search the web yourself.
Possible framing pattern: wording sets a specific interpretation frame rather than neutral description.
How score signals are formed
Bias score signal
Bias signal combines framing pressure, emotional wording, selective emphasis, and one-sided narrative markers.
Emotionality signal
Emotionality rises when evidence contains emotionally loaded wording and evaluative labels.
One-sidedness signal
One-sidedness rises when one frame dominates and alternative interpretations are weakly represented.
Evidence strength signal
Evidence strength rises with concrete claims, attributed statements, and verifiable contextual support.
Source A
39%
emotionality: 41 · one-sidedness: 35
Detected in Source A
appeal to fear
Source B
26%
emotionality: 27 · one-sidedness: 30
Detected in Source B
framing effect
Metrics
Framing differences
- Source A emotionality: 41/100 vs Source B: 27/100
- Source A one-sidedness: 35/100 vs Source B: 30/100
- Stance contrast: JEvaluations in this section were run on a fixed, randomly sampled subset of examples, and these scores should not be compared with publicly reported benchmarks on the same task. Alternative framing: Reasoning models are said be more accurate and less hallucinatory because they apply more computing power to solving a question, which is why o3 and o4-mini's hallucination rates were somewhat baffling.
Possible omitted/downplayed context
- Review which economic and policy factors each source keeps outside focus.
- Check whether alternative explanations are acknowledged.