We asked 18 AI models to forecast the 2026 midterms. Here is the board.
We asked 18 AI models to forecast the 2026 midterms. Here is the board. TL;DR: 18 models produced 198 forecasts across 11 questions between 26 August and 31 October 2026. The snapshot was sealed before the vote. 13 of
We asked 18 AI models to forecast the 2026 midterms. Here is the board.
TL;DR: 18 models produced 198 forecasts across 11 questions between 26 August and 31 October 2026. The snapshot was sealed before the vote. 13 of 18 models expect a Democratic House, 12 of 18 expect a Republican Senate, and the 2027 French presidential election has no consensus. This is a quality score, not an accuracy score: the votes are not counted yet.
The full hub is at elections.lforla.org.
Why election forecasting is a useful benchmark
Most LLM benchmarks ask a model to produce an answer that a judge can only rank. Election forecasting is different in three ways.
First, it is verifiable. The result exists on a known date. A forecast made before the vote can be scored against the count afterwards, without interpretation.
Second, it is probabilistic. A useful forecast is not just a winner; it is a winner with a probability. That makes calibration measurable, not just accuracy.
Third, it is pre-registered. The questions, the answers and the collection dates are fixed before the event. If the answers can move after the first results, the exercise is theatre.
We collected the forecasts between 26 August and 31 October 2026. The snapshot was sealed on 31 October. Nothing was edited after that date.
What we asked
The benchmark covers 11 questions across two elections.
US 2026 midterms
- Which party controls the House?
- Which party controls the Senate?
- What is the Senate seat balance?
- Which party wins the most governorships?
- Which party wins the combined House popular vote?
France 2027 presidential election
- Who wins the presidency?
- Who is the National Rally candidate?
- Who is the outgoing-majority candidate?
- Who is the left candidate?
- Who reaches the second round?
- What is first-round turnout?
Each model answers in French, with a named candidate or party, a stated probability and a short justification. Answers are published verbatim.
The board
House: a Democratic consensus, with one holdout
13 of 18 models expect the Democratic Party to control the House after 3 November. The median stated probability is 65%.
One model, Nemotron 3.5 Lightning, puts Republicans ahead. The House seat claims range from 200 to 235 seats; 218 is the majority line. The consensus is real, but it is not unanimous.
Senate: Republicans favoured, but not unanimously
12 of 18 models see Republicans holding the Senate. Six see a Democratic flip. The median probability is 69%.
The seat calls cluster between 51 and 54 Republican seats. Two models put Democrats at 52. The Senate is the race where the board is most divided, and the one where a single state can invalidate the consensus.
France 2027: no consensus candidate
The 2027 French presidential election is still months away. The board reflects that uncertainty.
- Jordan Bardella: 7 models name him first
- Marine Le Pen: 5 models
- Γdouard Philippe: 1 model
- Another candidate: 5 models, mostly the outgoing majority's standard-bearer
No candidate has a majority of the board. That is not a failure of the models; it is an honest reading of an election that has not started.
The quality scores
Before the vote, each answer is scored on three axes, 0 to 100: specificity, grounding and calibration. The overall score is the mean of the two categories. A refusal scores 0.
At the time of publication, the top of the board is:
| Model | Quality score |
|---|---|
| Nemotron 3 Ultra (free) | 89.10 |
| DeepSeek V4.1 Flash | 82.10 |
| Claude Opus 5.5 | 80.62 |
| GLM 5.2 | 78.00 |
| HY3 (free) | 76.50 |
| GPT-5.5 | 76.10 |
| GPT-6 Luna | 75.36 |
This ranking measures the quality of the forecast, not whether it is right. A model can be wrong and score well. A vague answer always scores badly.
The judge is a fixed model, DeepSeek V4 Pro at temperature 0. That is a deliberate choice and a disclosed limitation: several models under test are from the same family as the judge. We publish the protocol rather than hide it.
What happens after the vote
Once results are certified, we add a second layer:
- Winner accuracy: did the model call the right party or candidate?
- Calibration: did 70% calls happen about 70% of the time?
- Brier score: the mean squared error of the stated probabilities.
The Brier score is the part that separates a lucky call from a good forecast. A confident wrong call costs more than a hedged one. A 70% call that misses contributes 0.49; a 55% call that misses contributes 0.3025. Uncertainty is only rewarded when it is honest.
Both layers will be published side by side. Quality and accuracy are never conflated.
Sealed before the vote
The snapshot is sealed with SHA-256:
26c1563ea12200b9436e9ab7fd03b1af7f59fd17aea5697a2bd96a25bd38b2d9
Git tag: elections-forecasts-2026-10-31.
The file is verticals/elections/data/forecasts.json in the LFORLA repository. You can verify it with:
sha256sum verticals/elections/data/forecasts.json
The results page is intentionally pending. We use official sources only: state election offices, then the FEC and the Clerk of the House. No AP, no DDHQ, no media race calls. Certification takes days, and the benchmark scores forecasts against the count, not against the first projection.
Read the full board
- Election forecast hub
- US 2026 forecasts
- France 2027 forecasts
- Methodology
- Results page
- Full leaderboard
Originally published at elections.lforla.org.
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes β full credit and traffic to the original publisher.