AI models for every need
BiasChecker lets you choose from 21 AI models by 10 different makers. Every model is trained on different data and carries its own biases, so having access to diverse models matters for bias analysis regardless of model power. Pick based on the depth you need and how many articles you want to analyse per month.
How credit cost works
A typical news article (~500 words) uses about 2,500 credits per analysis with a 1× model. The multiplier tells you how much more expensive a model is compared to baseline. A 4× model uses 10,000 credits for the same article, a 10× model uses 25,000.
Grok 4.1 Fast
xAI
Best for: Everyday analysis — the best starting point for most articles
Qwen 3 32B
Alibaba
Best for: Non-English articles or when you want deeper analysis at no extra cost
GPT-OSS 120B
OpenAI
Best for: A strong all-rounder when you want OpenAI quality at budget price
GLM 4.7 Flash
Zhipu AI
Best for: Maximum volume — when you want to analyse as many articles as possible
Gemini 2.5 Flash Lite
Best for: Quick factual checks at baseline cost
GPT-5 Mini
OpenAI
Best for: Scientific and legal analyses where accuracy matters more than cost
Kimi K2
Moonshot
Best for: Moral and ethical assessment with thorough analysis
Kimi K2.5
Moonshot
Best for: Upgraded alternative to Kimi K2 for complex articles
Gemini 2.5 Flash
Best for: Historical parallels and analyses needing broad world knowledge
GLM 4.7
Zhipu AI
Best for: Asian media analysis where cultural context matters
Nova 2 Lite
Amazon
Best for: Consistent general-purpose analysis
Llama 3.3 70B
Meta
Best for: Manipulation analysis from an independent open-source perspective
Gemini 3 Flash
Best for: Latest Google capabilities at a moderate price point
DeepSeek V3.2
DeepSeek
Best for: Critical analysis and logical fallacy detection
Claude Haiku 4.5
Anthropic
Best for: Moral assessment — strong at ethical nuance
DeepSeek R1
DeepSeek
Best for: Revealing hidden intent — deeper analysis uncovers subtle subtext
GPT-5.1
OpenAI
Best for: When you need the most thorough analysis from OpenAI
Gemini 2.5 Pro
Best for: Scientific analysis — best at evaluating evidence and methodology
Gemini 3.1 Pro
Best for: Maximum Google quality — latest capabilities for important articles
Claude Sonnet 4.6
Anthropic
Best for: Nuanced, balanced analysis on sensitive or controversial topics
Grok 4
xAI
Best for: High-quality analysis when reasoning models are overkill
Quick comparison
Credits per article based on a typical ~500-word news article (~2,500 raw tokens).
| Model | Maker | Multiplier | Credits per article | Articles on Pro plan |
|---|---|---|---|---|
| Grok 4.1 Fastdefault | xAI | 1× | 2,500 | ~2,000 |
| Qwen 3 32B | Alibaba | 1× | 2,500 | ~2,000 |
| GPT-OSS 120B | OpenAI | 1× | 2,500 | ~2,000 |
| GLM 4.7 Flash | Zhipu AI | 1× | 2,500 | ~2,000 |
| Gemini 2.5 Flash Lite | 1× | 2,500 | ~2,000 | |
| GPT-5 Mini | OpenAI | 2× | 5,000 | ~1,000 |
| Kimi K2 | Moonshot | 3× | 7,500 | ~666 |
| Kimi K2.5 | Moonshot | 3× | 7,500 | ~666 |
| Gemini 2.5 Flash | 3× | 7,500 | ~666 | |
| GLM 4.7 | Zhipu AI | 3× | 7,500 | ~666 |
| Nova 2 Lite | Amazon | 3× | 7,500 | ~666 |
| Llama 3.3 70B | Meta | 2× | 5,000 | ~1,000 |
| Gemini 3 Flash | 4× | 10,000 | ~500 | |
| DeepSeek V3.2 | DeepSeek | 3× | 7,500 | ~666 |
| Claude Haiku 4.5 | Anthropic | 5× | 12,500 | ~400 |
| DeepSeek R1 | DeepSeek | 7× | 17,500 | ~285 |
| GPT-5.1 | OpenAI | 10× | 25,000 | ~200 |
| Gemini 2.5 Pro | 10× | 25,000 | ~200 | |
| Gemini 3.1 Pro | 10× | 25,000 | ~200 | |
| Claude Sonnet 4.6 | Anthropic | 10× | 25,000 | ~200 |
| Grok 4 | xAI | 5× | 12,500 | ~400 |
Pro plan = 5,000,000 credits/month. Lite plan = 2,000,000 credits/month. Trial = 200,000 credits. Actual usage varies slightly by article length.
See the difference: same article, different models
We analysed the same article with multiple models across all nine analysis types. Notice how models can reach different conclusions — and how each brings a unique perspective.
Example article
Listening to the Voices of the School System
A qualitative report on COVID-19 lockdown impacts on students, parents, and teachers — drawing on discussion groups to argue for relational recovery in schools.
Relationships FoundationEvaluates evidence quality, logical reasoning, and identifies unsupported claims.
This article discusses the emotional and relational impact of the COVID-19 lockdown on students, parents, and teachers, based on a qualitative study. It highlights the importance of relationships and the potential for transformation in the education system.
Assumptions
- • The qualitative data gathered from discussion groups is representative of the broader school system\'s experiences.
- • The \'portal\' metaphor accurately captures the transformative potential of the pandemic for education.
- • Focusing on relationships and emotional well-being is a sufficient strategy for educational recovery.
- • The insights from the first lockdown are directly applicable to future educational challenges.
- • The proposed transformation of the education system is achievable through reflection and learning from disruption.
Substantive Assessment
Core issue: The article addresses the profound emotional and relational impact of the COVID-19 lockdown on the school system and the potential for this crisis to catalyze transformation.
Key gaps: The article lacks specific, actionable recommendations for how the education system can 'transform' or 'rethink' its practices beyond general calls for reflection. It also doesn't deeply explore the practical challenges of implementing such changes or the potential resistance to them.
Recommendations
- • Develop concrete strategies and frameworks for implementing the \'transformation\' suggested by the \'portal\' metaphor.
- • Explore how to balance the focus on relationships and emotional well-being with the practical needs of academic recovery and assessment.
- • Investigate the long-term impacts of the pandemic on different student demographics to ensure equitable recovery and support.
The article argues that post‑COVID school recovery should prioritize relational and emotional needs of students, parents, and teachers, based on a small qualitative study. While the focus on relationships is valuable, the piece over‑generalises from limited data and lacks concrete, actionable recommendations.
Assumptions
- • The small group of interviewees represents the broader national school community.
- • Emotional and relational recovery will automatically translate into improved academic outcomes.
- • Stakeholders are ready and able to prioritize relational change over curriculum catch‑up.
- • The pandemic creates a unique ‘portal’ that can be leveraged for systemic educational reform.
- • Qualitative, psychodynamic analysis is sufficient to guide policy decisions.
Issues
The claim that “the news‑media suggests we should be concerned about more important things like catching up on ‘lost learning’” is presented without any evidence of media coverage.
“the news‑media suggests we should be concerned about more important things like catching up on ‘lost learning’ and assessment points”
Findings from a series of discussion groups in late 2020 are used to assert that the relational purpose of schools is “highly valued” and “greatly missed” by all stakeholders, despite no information on sample size or representativeness.
“That the social and relational purpose of schools is highly valued by students, parents and teachers alike, and was greatly missed during the first lockdown.”
The article does not provide details on the methodology (e.g., number of participants, demographic spread) needed to assess the credibility of the qualitative insights.
“Using a qualitative approach informed by both systems and psychodynamic theory, we set out to learn how those in and around schools …”
The piece suggests that focusing on relationships is sufficient for school recovery, downplaying the simultaneous need for academic catch‑up and assessment remediation.
“People are itching to re‑engage with the relationships that lie at the heart of their identities after such a long, painful spell of disruption.”
Substantive Assessment
Core issue: How to shape post‑pandemic school recovery to address the emotional and relational needs of students, parents, and teachers.
Key gaps: Missing data on sample size and diversity, no concrete recommendations for schools or policymakers, and no integration of relational focus with academic remediation plans.
Recommendations
- • Commission a mixed‑methods study with a nationally representative sample to validate the relational findings.
- • Develop a clear policy framework that balances emotional wellbeing initiatives with academic catch‑up strategies.
- • Translate qualitative insights into actionable steps (e.g., teacher‑student mentorship programs, parental support hubs) and allocate resources accordingly.
The piece summarizes qualitative findings from discussion groups after the first COVID-19 lockdown and argues the school system should prioritise relational recovery and use the disruption as an opportunity to rethink education through a new Co-mission. It privileges lived experience and emotional impacts but provides little methodological detail or concrete, actionable plans for system change.
Feasibility
A Co-mission and system redesign are plausible but will require clear mandate, funding, representative governance, measurable objectives and phased pilots; without these the proposal risks remaining rhetorical rather than deliverable.
Assumptions
- • Findings from the discussion groups reflect wider national experience and needs.
- • Prioritising relational recovery will not significantly delay or conflict with necessary academic catch-up.
- • Schools and staff have capacity and willingness to adopt transformational changes without commensurate additional resources.
- • A voluntary Co-mission will have sufficient authority to influence statutory education policy and inspection frameworks.
Issues
Methodological detail is absent (sample size, selection, demographics, analysis approach), so it's unclear how widely the qualitative findings generalise across diverse schools and communities.
“we invited them to a series of discussion groups in late 2020 to talk about their experiences”
“Using a qualitative approach informed by both systems and psychodynamic theory”
Conclusions about the 'system' and widespread parental/teacher/child feelings appear to be drawn from limited qualitative work without acknowledging representativeness or alternative experiences.
“That the social and relational purpose of schools is highly valued by students, parents and teachers alike”
Calls to 'rethink how schooling is done' and to create a Co-mission lack operational detail: governance, timeline, resources, success metrics and how tensions (wellbeing vs. catch-up) will be resolved.
“Big Change is using insights from this work to inform the creation of a new Co-mission on the Purpose and Future of Education”
The piece foregrounds relational recovery as central to recovery but downplays or omits how this will interact with measurable learning loss, curriculum demands, accountability regimes and resource constraints.
“Whilst the news-media suggests we should be concerned about more important things like catching up on ‘lost learning’”
“There is an opportunity amidst all this to change the nature of the game”
The article emphasises parents, teachers and children but insufficiently addresses other critical stakeholders (local authorities, unions, SEND specialists, inspection bodies, funding authorities) whose buy-in is necessary for system change.
“we set out to learn how those in and around schools – the parents, teachers and children – relate”
Substantive Assessment
Core issue: The central matter is how schools should prioritise relational and emotional recovery after lockdown and whether the pandemic should trigger systemic rethinking of schooling.
Key gaps: Missing are methodological transparency, scale and representativeness of evidence, concrete policy levers, resource and workforce implications, timelines, measurable outcomes, and strategies for reconciling wellbeing-centred change with accountability and curriculum recovery demands.
Alternatives Suggested
Phased pilots combining relational recovery with targeted tutoring
Trade-offs: Slower national rollout but provides measurable outcomes and reduces policy risk.
Create an independent, time-limited taskforce with statutory partners
Trade-offs: May be politically contentious and require negotiation, but improves legitimacy and feasibility.
Recommendations
- • Publish full methodology (sample size, recruitment, demographics, analytic method) and triangulate qualitative findings with broader quantitative data on attainment and wellbeing.
- • Define clear, timebound objectives and metrics for any Co-mission (e.g., pilot sites, outcome measures, budget and decision rules) and include statutory partners and SEND/union representation.
- • Pilot relational-centred interventions paired with targeted catch-up tutoring in diverse contexts to generate evidence on impact, cost and scalability before advocating system-wide redesign.
The text introduces a report by the Relationships Foundation and Big Change that uses qualitative research to explore the emotional and relational impact of the COVID-19 pandemic on students, parents, and teachers. It argues that the pandemic serves as a 'portal' to rethink the social and relational purpose of schooling beyond academic 'catch-up' metrics.
Feasibility
The proposal to use the pandemic as a 'portal' for systemic transformation is conceptually strong but lacks a concrete roadmap for implementation within the existing rigid educational framework.
Assumptions
- • Qualitative insights from discussion groups are sufficient to inform national-level systemic transformation.
- • The \'relational buzz\' of school is universally positive for all students, including those who may have thrived in remote environments.
- • School leaders have the institutional autonomy to prioritize emotional renewal over government-mandated assessment points.
- • The emotional experiences of the first lockdown (2020) remain the primary drivers for policy in 2021 and beyond.
Issues
The text frames the tension between 'lost learning' and 'relational needs' as a binary choice, potentially overlooking how academic recovery and emotional wellbeing are interdependent.
“Whilst the news-media suggests we should be concerned about more important things like catching up on ‘lost learning’... the focus... seems to be more straightforward.”
The report draws broad conclusions about the 'school system' based on a qualitative sample that may not represent the full diversity of socioeconomic pressures faced by families.
“we saw just the tip of the iceberg. There was support for the process of allocation by schools of resources to families... and yet the sense that one... were ‘insignificant’”
Substantive Assessment
Core issue: The central issue is the potential for the education system to revert to a high-pressure, data-driven status quo post-pandemic, ignoring the profound emotional and relational shifts experienced by its stakeholders.
Key gaps: The text fails to address the structural barriers to change, such as standardized testing requirements, funding formulas, and the specific needs of neurodivergent students who may have found the 'relational buzz' of school overstimulating.
Alternatives Suggested
Integrated Recovery Model
Trade-offs: Requires higher initial staffing costs and more complex scheduling than a purely relational or purely academic focus.
Recommendations
- • Develop a concrete policy framework that translates \'relational value\' into measurable school climate standards.
- • Conduct follow-up quantitative studies to validate the qualitative findings across a broader demographic spectrum.
- • Create specific guidance for school leaders on balancing statutory assessment requirements with the recommended \'space for reflection and renewal\'.
The piece is a reflective, qualitative account of how students, parents, and teachers experienced school closures during Covid-19 and argues that relationships and wellbeing should be central to post-pandemic educational reform. It is mainly descriptive, with a modest normative claim that the crisis is an opportunity to rethink the purpose and practice of schooling around relationships.
Feasibility
The call to center relationships and use the pandemic as a catalyst for rethinking schooling is directionally sensible but remains high-level and lacks concrete implementation steps or constraints, making feasibility difficult to judge beyond a general endorsement of its intent.
Assumptions
- • Relational experiences of the report’s participants are broadly representative of the wider school system’s experiences during lockdown.
- • Relational needs and wellbeing should be at least as central as academic outcomes in defining the purpose of schooling post-pandemic.
- • The pandemic-created disruption constitutes a genuine political and institutional opportunity window for significant educational transformation.
- • School cultures are readily reproduced in the home environment during home-schooling in ways that materially shape parents’ and children’s experiences.
- • Improved attention to relationships can be pursued without seriously compromising core academic objectives, or can be aligned with them.
Issues
The text repeatedly contrasts relational concerns with media focus on “lost learning” and assessments without engaging with the substantive reasons policymakers and the public might prioritize academic recovery (e.g., long-term earnings, inequality). This risks underplaying trade-offs between relational goals and academic objectives rather than acknowledging how they might be integrated.
““Whilst the news-media suggests we should be concerned about more important things like catching up on ‘lost learning’…””
““We hope that our new report… provides a reminder of what really matters to the humans in the school system””
The argument implicitly treats “relationships” as a largely unqualified good, without grappling with the diversity of school contexts (e.g., harmful peer dynamics, overburdened staff) or the complex ways relational and academic aims can conflict or reinforce each other. This flattens a multidimensional issue into a simple relational-versus-academic framing.
““People are itching to re-engage with the relationships that lie at the heart of their identities…””
““the social and relational purpose of schools is highly valued… and was greatly missed””
Some broad empirical-sounding generalizations are presented based on unspecified qualitative sampling, with little detail on sample size, demographics, or method, limiting the strength of system-wide inferences. This is acceptable for a blog but weakens any implied policy generalizations.
““Students, parents and teachers alike… greatly missed [the social and relational purpose of schools]””
““Parents struggled during lockdown, but their struggle was unequal.””
The piece advocates using the pandemic as a ‘portal’ to transform education and “change the nature of the game” but does not articulate specific reforms, mechanisms, or constraints, leaving a gap between diagnosis and actionable strategy. This makes it difficult for practitioners or policymakers to translate its insights into practice.
““There is an opportunity… to change the nature of the game; to rethink how schooling is done…””
““Big Change is using insights from this work to inform the creation of a new Co-mission…””
Substantive Assessment
Core issue: How the emotional and relational experiences of students, parents, and teachers during Covid-19 school closures should inform the future purpose and practice of schooling.
Key gaps: The article largely omits how relational priorities intersect with longstanding inequities (socioeconomic status, race, SEND, digital divide) or how different groups may need tailored support. It gives little attention to potential tensions between relational care, safeguarding concerns, and academic standards, and it does not consider possible unintended consequences of reorienting the system without adequate resources (e.g., overburdening teachers with additional relational expectations). Moreover, it offers minimal transparency about the research sample and methodology, which is important when extrapolating from qualitative insights to system-level claims.
Alternatives Suggested
Explicitly integrate relational priorities into academic recovery planning, framing relationships as an enabler of learning rather than an implicit counterweight to ‘lost learning’.
Trade-offs: Requires more nuance and may weaken the rhetorical contrast with media narratives, but yields more practical, coalition-building guidance.
Ground proposed transformation in specific, evidence-informed reforms (e.g., tutor/mentoring systems, reduced class sizes, structured parental engagement) linked to the qualitative findings.
Trade-offs: Demands more empirical backing and may expose disagreements about priorities or costs.
Present differentiated implications for varied school and family contexts (e.g., high-poverty schools, SEND populations, rural vs. urban) rather than system-wide generalizations.
Trade-offs: More complex narrative, less elegant messaging, but more actionable and just.
Recommendations
- • Clarify the scope and limits of the qualitative evidence—briefly describing sampling, diversity, and method—and temper system-wide generalizations accordingly, while pointing to where additional quantitative or mixed-methods research is needed.
- • Reframe the narrative to show how relational wellbeing and academic recovery can be mutually reinforcing, and outline 2–3 concrete, evidence-informed reforms (e.g., advisory systems, structured parental engagement, trauma-informed practice) that follow from the findings.
- • Explicitly discuss equity and variation across contexts—who was most affected and why—and link the proposed ‘portal’ for change to specific structural levers (funding, accountability, teacher workload) to move from evocative metaphor to actionable strategy.
Key takeaway: Budget Gemini 2.5 Flash Lite gave a clean pass with no issues, while GPT-5 Mini (2×) found 5 issues with evidence, and GPT-5.1 (10×) delivered the most detailed critique with substantive assessment, alternatives, and concrete recommendations. Premium models dig deeper.
Our recommendations
🏃 Best for everyday use
Grok 4.1 Fast (1×) — the default for good reason. Fast, accurate, and lets you analyse the most articles per month. Start here and only switch models when you have a specific need.
🔬 Best for scientific & legal analysis
GPT-5 Mini (2×) or Gemini 2.5 Pro (10×) — internal reasoning helps these models evaluate evidence, methodology, and legal compliance more carefully.
⚖️ Best for moral & ethical assessment
Claude Haiku 4.5 (5×) or Claude Sonnet 4.6 (10×) — Anthropic models are trained with a strong focus on safety and ethical reasoning, making them excellent at nuanced moral analysis.
🔍 Best for uncovering hidden intent
DeepSeek R1 (7×) or Grok 4 (10×) — chain-of-thought reasoning models that think step by step before answering, making them better at uncovering subtle manipulation and hidden subtext.
🌍 Best for non-English content
Qwen 3 32B (1×) — strong multilingual support, especially for Chinese, Arabic, and other non-Latin script languages. At baseline cost, it's a great choice for international media.
Frequently asked questions
Which model should I start with?
Grok 4.1 Fast — it's the default for a reason. Excellent quality at baseline cost means you can analyse more articles per month. Switch to a premium model only when you want extra depth on an important article.
Can I use different models for different analysis types?
Yes. In the Chrome extension settings, switch to "per-analysis" mode to assign a different model to each of the nine analysis types. For example, use Grok 4.1 Fast for everyday bias checks and Claude Sonnet for moral assessment.
Do all plans get access to all models?
All paid plans (Lite and Pro) can use every model. The free trial uses the default model automatically. The only difference between plans is the monthly credit quota, not which models you can access.
Why do some models cost more?
More expensive models are generally larger, newer, or use reasoning techniques that require more computation. The quality difference is most noticeable on nuanced or politically complex content — for straightforward articles, a 1× model does a great job.
What are "credits"?
Credits are the units that measure your usage. Each analysis consumes credits based on the article length and the model's multiplier. A typical article uses about 5,000 credits at 1× cost. Your plan gives you a monthly credit allowance that resets each billing cycle.