No single list comes close to the 257-seat majority in the Chamber. Updated October 2, 2026.
513 seats allocated proportionally across the 27 federal units, list by list. Seat distribution of the three leading lists over 20,000 simulations.
Hover over or tap a state to see the details.
Color of the list expected to top the vote in each state.
Map, 27 states and detailed cards →
| List | Expected seats | 90% | 2022 | Largest party |
|---|---|---|---|---|
| União Progressista | 106.3 | 64 to 156 | 104 | 56% |
| PL | 88.3 | 50 to 135 | 98 | 27% |
| PT federation | 75.0 | 40 to 118 | 82 | 15% |
| Republicanos | 50.4 | 25 to 82 | 40 | 2% |
| PSD | 43.1 | 21 to 71 | 42 | |
| MDB | 36.5 | 17 to 60 | 41 | |
| Podemos | 30.1 | 13 to 52 | 20 | |
| PSB | 16.6 | 7 to 30 | 15 | |
| PSDB-Cidadania | 16.4 | 7 to 30 | 18 | |
| PSOL-Rede | 16.3 | 7 to 28 | 15 | |
| Renovação Solidária | 14.1 | 5 to 27 | 12 | |
| PDT | 8.3 | 3 to 16 | 16 | |
| Avante | 6.1 | 2 to 12 | 7 | |
| Novo | 4.7 | 1 to 10 | 3 | |
| Missão | 0.9 | 0 to 2 | 0 |
2022: seats won in 2022 by the parties that make up the 2026 list.
1,059 seats in total, including the Federal District’s Legislative Chamber.
| List | Expected seats | 90% | Largest party (27 assemblies combined) |
|---|---|---|---|
| União Progressista | 192.3 | 120 to 281 | 55% |
| PT federation | 158.1 | 94 to 236 | 24% |
| PL | 145.7 | 86 to 220 | 16% |
| MDB | 114.2 | 68 to 169 | 3% |
| PSD | 91.6 | 51 to 143 | <1% |
| Republicanos | 88.6 | 49 to 139 | <1% |
| Podemos | 49.8 | 26 to 81 | |
| PSDB-Cidadania | 47.9 | 25 to 78 | |
| PSB | 45.9 | 24 to 74 | |
| Renovação Solidária | 37.1 | 18 to 62 | |
| PSOL-Rede | 27.0 | 12 to 47 | |
| PDT | 25.4 | 12 to 43 | |
| Avante | 15.5 | 7 to 28 | |
| Novo | 9.8 | 3 to 20 | |
| Mobiliza | 5.6 | 1 to 11 | |
| Agir | 3.9 | 1 to 8 |
Two of the three seats are up in every state, 54 out of 81. Each voter casts two votes; the top two candidates are elected in a single round.
Hover over or tap a state to see the details.
Color of the best-placed candidate’s party.
Map, 27 races and race-by-race calculation →
| Party | Expected seats out of 54 | 90% |
|---|---|---|
| PL | 13.3 | 9 to 18 |
| MDB | 7.4 | 5 to 10 |
| PT | 6.7 | 3 to 10 |
| PP | 4.2 | 2 to 7 |
| União Brasil | 3.7 | 2 to 6 |
| PSB | 3.6 | 2 to 5 |
| PSD | 3.4 | 1 to 6 |
| Republicanos | 2.8 | 1 to 5 |
| Podemos | 2.1 | 1 to 3 |
| PDT | 2.0 | 0 to 4 |
| Novo | 1.6 | 0 to 3 |
| PSDB | 1.2 | 0 to 3 |
Chances of being elected. Ordered by the gap between second and third.
| State | Favorite | Second | Third |
|---|---|---|---|
| RO Rondônia | Dr Fernando Máximo PL 80% | Bruno Scheid PL 46% | Sílvia Cristina PP 45% |
| SE Sergipe | Delegado André David Republicanos 52% | Rogerio Carvalho PT 40% | Delegado Alessandro MDB 40% |
| SP São Paulo | Guilherme Derrite PP 55% | Marina Silva REDE 52% | Simone Tebet PSB 50% |
| CE Ceará | Cid Gomes PSB 74% | Capitão Wagner União Brasil 54% | Luizianne REDE 52% |
| PR Paraná | Deltan Dallagnol Novo 61% | Filipe Barros PL 45% | Alexandre Curi Republicanos 43% |
| ES Espírito Santo | Renato Casagrande PSB 96% | Fabiano Contarato PT 32% | Sergio Meneguelli PSD 30% |
| PI Piauí | Marcelo Castro MDB 79% | Júlio César o Julim do Lula PSD 60% | Ciro Nogueira PP 56% |
| SC Santa Catarina | Carol de Toni PL 71% | Esperidião Amin PP 57% | Carlos Bolsonaro PL 52% |
| AP Amapá | Rayssa Furlan Podemos 86% | Randolfe PT 48% | Lucas Barreto PSD 43% |
| RJ Rio de Janeiro | Benedita da Silva PT 75% | Carlos Jordy PL 42% | Carlos Portinho PL 36% |
| MA Maranhão | Roseana Sarney MDB 70% | Fufuca PP 44% | Lahesio Bonfim Novo 36% |
| TO Tocantins | Eduardo Gomes PL 80% | Alexandre Guimarães MDB 46% | Gaguim União Brasil 37% |
| DF Federal District | Michelle Bolsonaro PL 79% | Leila do Vôlei PDT 50% | Bia Kicis PL 41% |
| MG Minas Gerais | Marília Campos PT 63% | Carlos Viana PSD 47% | Domingos Sávio PL 37% |
| RS Rio Grande do Sul | Marcel van Hattem Novo 59% | Manuela d'Ávila PSOL 51% | Sanderson PL 39% |
| AL Alagoas | Arthur Lira PP 67% | Marina JHC PSDB 65% | Renan MDB 52% |
| RR Roraima | Teresa Surita MDB 74% | Nicoletti PL 59% | Helena da Asatur PSD 36% |
| AC Acre | Gladson Camelí PP 69% | Marcio Bittar PL 58% | Mara Rocha Republicanos 33% |
| BA Bahia | Rui Costa PT 79% | Jaques Wagner PT 60% | João Roma PL 33% |
| PA Pará | Helder MDB 83% | Delegado Éder Mauro PL 57% | Chicão União Brasil 29% |
| MT Mato Grosso | Mauro Mendes União Brasil 91% | Janaina Riva MDB 62% | Zé Medeiros PL 31% |
| GO Goiás | Gracinha Caiado União Brasil 79% | Gustavo Gayer PL 64% | Dr Zacharias Calil MDB 33% |
| PE Pernambuco | Marília Arraes PDT 82% | Humberto Costa PT 66% | Mendonça Filho PL 34% |
| AM Amazonas | Eduardo Braga MDB 85% | Capitão Alberto Neto PL 66% | Wilson Lima União Brasil 26% |
| PB Paraíba | Joao Azevêdo PSB 97% | Veneziano MDB 73% | Nabor Republicanos 26% |
| RN Rio Grande do Norte | Styvenson Valentim Podemos 90% | Zenaide Maia PSD 68% | Samanda de Lula PT 17% |
| MS Mato Grosso do Sul | Reinaldo Azambuja PL 91% | Capitão Contar PL 82% | Vander Loubet PT 18% |
27 posts. Elected in the first round with over half the valid votes, otherwise a runoff on October 25.
Hover over or tap a state to see the details.
Color of the favorite’s party. Darker shades mean higher chances.
Map, 27 races and calculation card by state →
| Party | Expected governorships out of 27 | 90% |
|---|---|---|
| PSD | 5.3 | 4 to 7 |
| PL | 5.2 | 4 to 7 |
| Republicanos | 3.6 | 2 to 5 |
| PP | 3.5 | 3 to 4 |
| MDB | 2.6 | 1 to 4 |
| União Brasil | 2.6 | 2 to 3 |
| PT | 1.9 | 1 to 3 |
| PSDB | 1.2 | 0 to 2 |
| Podemos | 0.5 | 0 to 1 |
| PDT | 0.3 | 0 to 1 |
| PSB | 0.3 | 0 to 1 |
| State | Favorite | Main rival | Favorite’s chances | Wins in round 1 |
|---|---|---|---|---|
| AC Acre | Mailza Assis PP | Alan Rick Republicanos | 51% | 18% |
| PA Pará | Dr Daniel Podemos | Hana Ghassan MDB | 52% | 43% |
| CE Ceará | Ciro Gomes PSDB | Elmano de Freitas PT | 55% | 47% |
| BA Bahia | ACM Neto União Brasil | Jerônimo Rodrigues PT | 56% | 42% |
| AL Alagoas | JHC PSDB | Renan Filho MDB | 66% | 60% |
| MT Mato Grosso | Otaviano Pivetta Republicanos | Wellington Fagundes PL | 68% | 22% |
| RS Rio Grande do Sul | Zucco PL | Juliana Brizola PDT | 68% | 21% |
| ES Espírito Santo | Ricardo Ferraço MDB | Lorenzo Pazolini Republicanos | 68% | 18% |
| PE Pernambuco | Raquel Lyra PSD | João Campos PSB | 70% | 51% |
| AM Amazonas | Omar Aziz PSD | Professora Maria do Carmo PL or Roberto Cidade (União Brasil) | 78% | 4% |
| SE Sergipe | Fábio PSD | Valmir de Francisquinho Republicanos | 82% | 46% |
| MA Maranhão | Eduardo Braide PSD | Orleans Brandão MDB | 87% | 16% |
| RJ Rio de Janeiro | Eduardo Paes PSD | Douglas Ruas PL | 91% | 37% |
| RO Rondônia | Marcos Rogério PL | Adailton Furia PSD | 91% | 26% |
| PR Paraná | Sergio Moro PL | Requião Filho PDT or Sandro Alex (PSD) | 93% | 39% |
| RN Rio Grande do Norte | Allyson União Brasil | Álvaro Dias PL or Cadu de Lula (PT) | 93% | 29% |
| DF Federal District | Celina Leão PP | Arruda PSD or Leandro Grass (PT) | 94% | 25% |
| SP São Paulo | Tarcísio Republicanos | Fernando Haddad PT | 96% | 78% |
| AP Amapá | Dr Furlan PSD | Clécio União Brasil | 98% | 89% |
| MG Minas Gerais | Cleitinho Azevedo Republicanos | Patrus Ananias PT | 98% | 31% |
| RR Roraima | Arthur Henrique PL | Soldado Sampaio Republicanos | >99% | 68% |
| GO Goiás | Daniel Vilela MDB | Marconi Perillo PSDB or Wilder Morais (PL) | >99% | 36% |
| PI Piauí | Rafael Fonteles PT | Joel Rodrigues PP | >99% | 96% |
| PB Paraíba | Lucas Ribeiro PP | Efraim Filho PL | >99% | 98% |
| MS Mato Grosso do Sul | Eduardo Riedel PP | Fábio Trad PT | >99% | 78% |
| SC Santa Catarina | Jorginho Mello PL | João Rodrigues PSD | >99% | 79% |
| TO Tocantins | Professora Dorinha União Brasil | Laurez Moreira PSD | >99% | 95% |
Expected seats and governorships, week by week.
The Chamber and assembly model was tested on the 2022 election, without knowing the result. Applied to the actual 2022 votes, the seat calculation reproduces 1542 seats out of 1572.
| List | Centre | 90% | Actual 2022 | |
|---|---|---|---|---|
| PL | 65 | 34–108 | 98 | ● |
| PT federation | 81 | 44–130 | 82 | ● |
| União Brasil | 66 | 34–111 | 57 | ● |
| PP | 43 | 20–77 | 47 | ● |
| PSD | 41 | 22–69 | 42 | ● |
| MDB | 32 | 14–59 | 41 | ● |
| Republicanos | 30 | 12–62 | 40 | ● |
| PSDB-Cidadania | 31 | 15–55 | 18 | ● |
| PDT | 16 | 7–31 | 16 | ● |
| PSB | 24 | 10–47 | 15 | ● |
● result within range · ◇ outside. For governors and the Senate, the scorecard will be computed on the October 4 vote, from the forecast frozen the day before.
A predictive model is not one more poll. A poll interviews a sample and describes opinion at a given moment. A model interviews no one. It gathers what can be measured, published surveys, past election results and the electoral rules, and derives from them a distribution of possible outcomes, with their probability. It does not say a party will win so many seats. It says within which range it will finish, and what each candidate’s chances of election are.
Two pollsters published on the same day can differ by several points. The model aggregates every survey published in each state and registered with the Superior Electoral Court, taking into account their age, their size and the pollsters’ reliability.
Polls get it wrong, sometimes badly. The model builds in the size of the errors seen in past elections, without assuming in advance that one side will be underestimated. That is what gives each candidate their chances, and each list its range.
For each state, the model starts from the published polls. A governor is elected in the first round with over half the valid votes, otherwise a runoff takes place on October 25: the model simulates both. In the Senate, each voter has two votes and the top two are elected, in a single round.
Brazil elects its deputies by proportional representation, state by state, under a complex allocation rule. The model starts from the last election’s results and the make-up of the 2026 lists, then applies the official rule. Tested on the 2022 votes, it recovers 1542 of the 1572 seats actually allocated.
The October 4 elections are not independent. The vote for lists close to the two leading presidential candidates moves with the presidential vote, and the model takes this into account.
A single forecast would give a false sense of certainty. The model therefore replays each race twenty thousand times, varying what is genuinely uncertain. The probability shown is the share of simulations in which the event occurs.
Uncertainty is this model’s product, never its flaw. The simulations are reproducible, and the forecast on the eve of the vote is frozen, then compared with the official results.
Election results and candidacies: Superior Electoral Court. 2026 polls: surveys published by pollsters and registered with the Superior Electoral Court.
Export the forecasts as JSON ↗
All rights belong to Le Millénaire SAS, 47 rue de Vivienne, 75002 Paris.
Model designed by
Project director: William Thay
Deputy project director: Pierre Clairé
Political analysis and data: Matthieu Hocque
Data analysis, mathematics and validation: Florian Gérard-Mercier